CN115100541A - Satellite remote sensing data processing method and system and cloud platform - Google Patents

Satellite remote sensing data processing method and system and cloud platform Download PDF

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CN115100541A
CN115100541A CN202210856650.0A CN202210856650A CN115100541A CN 115100541 A CN115100541 A CN 115100541A CN 202210856650 A CN202210856650 A CN 202210856650A CN 115100541 A CN115100541 A CN 115100541A
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CN115100541B (en
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文娟
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Mizhi Yubao Beidou Agricultural Development Co ltd
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Abstract

The invention provides a satellite remote sensing data processing method, a satellite remote sensing data processing system and a cloud platform, and relates to the technical field of data processing. According to the method, the to-be-processed satellite remote sensing image is segmented according to the preset image segmentation size, so that multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image are output. And performing similarity calculation operation on every two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images to output the image similarity between every two frames of satellite remote sensing sub-images. And carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages. Based on the method, the problem that the storage effect of the satellite remote sensing data in the prior art is poor can be solved.

Description

Satellite remote sensing data processing method and system and cloud platform
Technical Field
The invention relates to the technical field of data processing, in particular to a satellite remote sensing data processing method and system and a cloud platform.
Background
On the basis of successful emission of various remote sensing earth observation satellites, multi-resolution, massive and real-time earth observation data related to the earth and various resource environments of the earth are continuously acquired. Moreover, with the development of remote sensing technology, the data size thereof is also increasing at an alarming rate, so that a large amount of satellite remote sensing data, i.e., satellite remote sensing images, is stored. When the satellite remote sensing image is stored, the satellite remote sensing image is generally segmented firstly and then stored respectively according to corresponding positions, so that the problem of poor storage effect may exist.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for processing satellite remote sensing data, and a cloud platform, so as to solve the problem in the prior art that the storage effect of the satellite remote sensing data is not good.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
a processing method of satellite remote sensing data is applied to a cloud platform and comprises the following steps:
according to a preset image segmentation size, carrying out segmentation operation on a satellite remote sensing image to be processed so as to output multi-frame satellite remote sensing subimages corresponding to the satellite remote sensing image, and splicing the multi-frame satellite remote sensing subimages to form the satellite remote sensing image;
performing similarity calculation operation on every two frames of satellite remote sensing subimages in the multiframe satellite remote sensing subimages to output the image similarity between every two frames of satellite remote sensing subimages;
and carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of performing a segmentation operation on the to-be-processed satellite remote sensing image according to a preset image segmentation size to output multiple frames of satellite remote sensing subimages corresponding to the satellite remote sensing image includes:
under the condition of receiving an original satellite remote sensing image, identifying whether a target storage request instruction for classified storage of the original satellite remote sensing image is received or not;
under the condition that a target storage request instruction for classified storage of the original satellite remote sensing image is identified and received, marking the original satellite remote sensing image as a satellite remote sensing image to be processed;
and carrying out segmentation operation on the satellite remote sensing image to be processed according to a preset image segmentation size so as to output a plurality of frames of satellite remote sensing sub-images corresponding to the satellite remote sensing image, wherein the image size of each frame of satellite remote sensing sub-image is equal to the image segmentation size.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of marking the original satellite remote sensing image as the satellite remote sensing image to be processed when it is recognized that the target storage request instruction for performing classified storage on the original satellite remote sensing image is received includes:
under the condition that a target storage request instruction for classified storage of the original satellite remote sensing image is identified, determining the image size of the original satellite remote sensing image so as to output the target image size corresponding to the original satellite remote sensing image;
comparing the size of the target image size with the image size reference value;
and under the condition that the size of the target image is smaller than or equal to the image size reference value, determining not to mark the original satellite remote sensing image as a satellite remote sensing image to be processed, and under the condition that the size of the target image is larger than the image size reference value, marking the original satellite remote sensing image as the satellite remote sensing image to be processed.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of performing a similarity calculation operation on every two frames of satellite remote sensing sub-images in the multiple frames of satellite remote sensing sub-images to output an image similarity between every two frames of satellite remote sensing sub-images includes:
carrying out image marking operation on one satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a target satellite remote sensing sub-image, and carrying out image marking operation on the other satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a reference satellite remote sensing sub-image;
performing object identification operation on the target satellite remote sensing subimage to output a target object type set, and performing object identification operation on the reference satellite remote sensing subimage to output a reference object type set, wherein each target object type included in the target object type set belongs to an object type corresponding to one object identified in the target satellite remote sensing subimage, and each reference object type included in the reference object type set belongs to an object type corresponding to one object identified in the reference satellite remote sensing subimage;
carrying out object contour extraction operation on the target satellite remote sensing sub-image to output a target object contour set, and carrying out object contour extraction operation on the reference satellite remote sensing sub-image to output a reference object contour set, wherein the target object contour set comprises a contour of each object extracted from the target satellite remote sensing sub-image, and the reference object contour set comprises a contour of each object extracted from the reference satellite remote sensing sub-image;
for each target object contour included in the target object contour set, performing contour similarity calculation on the target object contour and each reference object contour included in the reference object contour set, and performing mean value calculation operation on each contour similarity output by calculation to output a target contour similarity corresponding to the target object contour;
for each standard object type in a plurality of standard object types, carrying out statistical operation on the occurrence frequency of the standard object type in the target object type set to output a first occurrence frequency corresponding to the standard object type, and carrying out statistical operation on the occurrence frequency of the standard object type and the reference object type set to output a second occurrence frequency corresponding to the standard object type;
for each standard object type in a plurality of standard object types, performing fusion operation on a first occurrence frequency and a second occurrence frequency corresponding to the standard object type according to the number of the object types included in the target object type set and the number of the object types included in the reference object type set to output a corresponding target occurrence frequency, and then performing positive correlation value determination operation according to the target occurrence frequency to output the type importance corresponding to the standard object type;
for each target object contour, performing an updating operation on the target contour similarity corresponding to the target object contour according to the type importance degree corresponding to the object type corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour, and determining the type importance degree corresponding to the object type corresponding to the target object contour according to the minimum value of the type importance degrees corresponding to the standard object types when the object type corresponding to the target object contour does not belong to any standard object type of the standard object types;
and calculating and outputting the image similarity between the two frames of satellite remote sensing sub-images according to the updated contour similarity corresponding to each target object contour included in the target object contour set.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of performing, for each standard object type of the multiple standard object types, fusion operation on the first frequency of occurrence and the second frequency of occurrence corresponding to the standard object type according to the number of object types included in the target object type set and the number of object types included in the reference object type set to output the corresponding target frequency of occurrence, and then performing determination operation on a positive correlation value according to the target frequency of occurrence to output the type importance corresponding to the standard object type includes:
calculating and outputting a first weighting coefficient corresponding to the first frequency of occurrence and a second weighting coefficient corresponding to the second frequency of occurrence according to the number of the object types included in the target object type set and the number of the object types included in the reference object type set;
and for each standard object type in the plurality of standard object types, performing weighted mean calculation operation on the first occurrence frequency and the second occurrence frequency corresponding to the standard object type according to the first weighting coefficient and the second weighting coefficient to output target occurrence frequency corresponding to the standard object type, and then performing positive correlation value determination operation according to the target occurrence frequency to output type importance corresponding to the standard object type.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of updating the target contour similarity corresponding to each target object contour according to the type importance degree corresponding to the object type corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour includes:
for each target object contour, determining whether an object type corresponding to the target object contour belongs to any standard object type in the plurality of standard object types;
for each target object contour, when the object type corresponding to the target object contour does not belong to any standard object type in the standard object types, marking the minimum value in the multiple types of importance corresponding to the standard object types as the type importance corresponding to the target object contour, and when the object type corresponding to the target object contour belongs to one standard object type in the standard object types, marking the type importance corresponding to the standard object type as the type importance corresponding to the target object contour;
and for each target object contour, performing product calculation operation on the type importance degree corresponding to the target object contour and the target contour similarity corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of performing a classified storage operation on the multiple frames of satellite remote sensing subimages according to an image similarity between every two frames of satellite remote sensing subimages to store the multiple frames of satellite remote sensing subimages includes:
for each two frames of satellite remote sensing subimages in the multi-frame satellite remote sensing subimages, performing area distance calculation operation on the geographic areas corresponding to the two frames of satellite remote sensing subimages to output an area distance value between the two frames of satellite remote sensing subimages, and determining an area correlation value between the two frames of satellite remote sensing subimages according to the area distance value between the two frames of satellite remote sensing subimages to output an area correlation value between the two frames of satellite remote sensing subimages;
for each two frames of satellite remote sensing subimages in the multi-frame satellite remote sensing subimages, carrying out fusion operation on the area correlation value between the two frames of satellite remote sensing subimages and the image similarity between the two frames of satellite remote sensing subimages so as to output the image correlation value between the two frames of satellite remote sensing subimages;
and carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image correlation between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages.
In some preferred embodiments, in the method for processing satellite remote sensing data, the step of performing classified storage operation on the multiple frames of satellite remote sensing sub-images according to the image correlation between every two frames of satellite remote sensing sub-images to store the multiple frames of satellite remote sensing sub-images includes:
step 1, performing classification operation on the multiple frames of satellite remote sensing sub-images to form multiple remote sensing sub-image initial sets corresponding to the multiple frames of satellite remote sensing sub-images, wherein each remote sensing sub-image initial set comprises at least one frame of satellite remote sensing sub-image;
step 2, carrying out collection quantity statistics operation on the multiple remote sensing sub-image initial collections to output initial collection quantities corresponding to the multiple remote sensing sub-image initial collections, and then determining a first classification coefficient with a negative correlation relation according to the initial collection quantities;
step 3, for each remote sensing subimage initial set in the multiple remote sensing subimage initial sets, under the condition that the remote sensing subimage initial set comprises one frame of satellite remote sensing subimage, marking the remote sensing subimage initial set as a first remote sensing subimage initial set, assigning the target image correlation degree corresponding to the first remote sensing subimage initial set as the maximum image correlation degree, under the condition that the remote sensing subimage initial set comprises at least two frames of satellite remote sensing subimages, carrying out size comparison operation on the image correlation degree between each two frames of satellite remote sensing subimages included in the remote sensing subimage initial set and a correlation reference value, and marking the remote sensing subimage initial set as the first remote sensing subimage initial set under the condition that the image correlation degree between each two frames of satellite remote sensing subimages included in the remote sensing subimage initial set is greater than or equal to the correlation reference value, marking the average value of the image correlation degrees between every two frames of satellite remote sensing sub-images included in the initial remote sensing sub-image set as the target image correlation degree corresponding to the first initial remote sensing sub-image set;
step 4, under the condition that at least one initial set of remote sensing subimages in the multiple initial sets of remote sensing subimages is not marked as a first initial set of remote sensing subimages, skipping to the step 1, and under the condition that each initial set of remote sensing subimages in the multiple initial sets of remote sensing subimages is marked as a first initial set of remote sensing subimages, performing fusion operation on the target image correlation corresponding to each first initial set of remote sensing subimages to output a second classification coefficient;
step 5, carrying out fusion operation on the first classification coefficient and the second classification coefficient to output a target classification coefficient, and then comparing the target classification coefficient with a classification coefficient reference value;
and 6, under the condition that the target classification coefficient is smaller than or equal to the classification coefficient reference value, skipping to the step 1, under the condition that the target classification coefficient is larger than the classification coefficient reference value, marking the plurality of remote sensing sub-image initial sets formed by sequentially executing the step 1 as a plurality of corresponding remote sensing sub-image sets, and then respectively storing each remote sensing sub-image set.
The embodiment of the invention also provides a processing system of satellite remote sensing data, which is applied to a cloud platform, and the processing system of the satellite remote sensing data comprises:
the image segmentation module is used for carrying out segmentation operation on a satellite remote sensing image to be processed according to a preset image segmentation size so as to output multi-frame satellite remote sensing subimages corresponding to the satellite remote sensing image, and the multi-frame satellite remote sensing subimages are spliced to form the satellite remote sensing image;
the similarity calculation module is used for performing similarity calculation operation on each two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images so as to output the image similarity between each two frames of satellite remote sensing sub-images;
and the image classification storage module is used for performing classification storage operation on the plurality of frames of satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the plurality of frames of satellite remote sensing subimages.
The embodiment of the invention also provides a cloud platform, and the cloud platform is used for executing the satellite remote sensing data processing method.
According to the method, the system and the cloud platform for processing the satellite remote sensing data, provided by the embodiment of the invention, the to-be-processed satellite remote sensing image can be segmented according to the preset image segmentation size, so that multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image are output. And performing similarity calculation operation on every two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images to output the image similarity between every two frames of satellite remote sensing sub-images. And carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages. Based on the foregoing, the satellite remote sensing subimages formed by segmentation can be combined with the image similarity between the satellite remote sensing subimages, and classified storage operation is performed on the satellite remote sensing subimages, so that after storage, searching is facilitated (by classification, if the satellite remote sensing subimages with high image similarity are stored as one class, the satellite remote sensing subimages with high image similarity are searched as one class, or the satellite remote sensing subimages with low image similarity are searched as one class, so as to improve the diversity of the searched images), the control effect on storage can be improved to a certain extent, and the problem that the storage effect of the satellite remote sensing data in the prior art is poor can be solved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a structural block diagram of a cloud platform according to an embodiment of the present invention.
Fig. 2 is a schematic flowchart of steps included in a method for processing satellite remote sensing data according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of modules included in a system for processing satellite remote sensing data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a cloud platform.
In detail, in some possible implementation implementations, the cloud platform may include a memory and a processor. Wherein the memory and the processor may be directly or indirectly electrically connected to enable data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor can be used for executing the executable computer program stored in the memory, so as to realize the processing method of the satellite remote sensing data provided by the embodiment of the invention.
In detail, in some possible implementation manners, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like.
In detail, in some possible implementation embodiments, the Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
In detail, in some possible implementation embodiments, the cloud platform may be a server with data processing capability.
Referring to fig. 2, an embodiment of the present invention further provides a method for processing satellite remote sensing data, which can be applied to the cloud platform. The method steps defined by the relevant process of the satellite remote sensing data processing method can be realized by the cloud platform.
The specific process shown in FIG. 2 will be described in detail below.
And step S110, according to the preset image segmentation size, carrying out segmentation operation on the satellite remote sensing image to be processed so as to output multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image.
In the embodiment of the invention, the cloud platform can perform segmentation operation on the satellite remote sensing image to be processed according to the preset image segmentation size so as to output multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image. The multi-frame satellite remote sensing sub-images (formed according to the corresponding segmentation relationship) can be spliced to form the satellite remote sensing image.
And step S120, performing similarity calculation operation on every two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images to output the image similarity between every two frames of satellite remote sensing sub-images.
In the embodiment of the invention, the cloud platform can perform similarity calculation operation on every two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images so as to output the image similarity between every two frames of satellite remote sensing sub-images.
And S130, performing classified storage operation on the multiple frames of satellite remote sensing sub-images according to the image similarity between every two frames of satellite remote sensing sub-images so as to store the multiple frames of satellite remote sensing sub-images.
In the embodiment of the invention, the cloud platform can perform classified storage operation on the plurality of frames of satellite remote sensing sub-images according to the image similarity between every two frames of satellite remote sensing sub-images so as to store the plurality of frames of satellite remote sensing sub-images.
Based on the method, namely the processing method of the satellite remote sensing data, the to-be-processed satellite remote sensing image can be segmented according to the preset image segmentation size, so that multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image are output. And performing similarity calculation operation on every two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images to output the image similarity between every two frames of satellite remote sensing sub-images. And carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages. Based on the above content, the satellite remote sensing sub-images formed by segmentation can be combined with image similarity between the satellite remote sensing sub-images to perform classified storage operation, so that after storage, searching is facilitated (through classification, for example, high image similarity is stored as a class, and satellite remote sensing sub-images with high image similarity are searched together, or the first class of image similarity is stored together, and satellite remote sensing sub-images with low image similarity are searched together to improve diversity of searched images), and a control effect on storage can be improved to a certain extent, so that the problem of poor storage effect of satellite remote sensing data in the prior art can be solved.
In detail, in some possible implementation manners, in step S110 included in the foregoing method, the following details may be further included:
under the condition of receiving an original satellite remote sensing image, identifying whether a target storage request instruction for classified storage of the original satellite remote sensing image is received or not;
under the condition that a target storage request instruction for classified storage of the original satellite remote sensing image is identified and received, marking the original satellite remote sensing image as a satellite remote sensing image to be processed;
and according to a preset image segmentation size, performing segmentation operation on the satellite remote sensing image to be processed to output a plurality of frames of satellite remote sensing subimages corresponding to the satellite remote sensing image, wherein the image size of each frame of satellite remote sensing subimage is equal to the image segmentation size.
In detail, in some possible implementation embodiments, the step of marking the original satellite remote sensing image as the to-be-processed satellite remote sensing image when recognizing that the target storage request instruction for performing classified storage on the original satellite remote sensing image is received may further include the following specific contents:
under the condition that a target storage request instruction for classified storage of the original satellite remote sensing image is identified, determining the image size of the original satellite remote sensing image so as to output the target image size corresponding to the original satellite remote sensing image;
comparing the size of the target image size with the size of the image size reference value;
and under the condition that the size of the target image is smaller than or equal to the image size reference value, determining not to mark the original satellite remote sensing image as a satellite remote sensing image to be processed, and under the condition that the size of the target image is larger than the image size reference value, marking the original satellite remote sensing image as the satellite remote sensing image to be processed.
In detail, in some possible implementation manners, in step S120 included in the foregoing method, the following details may be further included:
carrying out image marking operation on one satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a target satellite remote sensing sub-image, and carrying out image marking operation on the other satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a reference satellite remote sensing sub-image;
performing object recognition operation on the target satellite remote sensing subimage to output a target object type set, and performing object recognition operation on the reference satellite remote sensing subimage to output a reference object type set, wherein each target object type included in the target object type set belongs to an object type corresponding to one object identified in the target satellite remote sensing subimage, and each reference object type included in the reference object type set belongs to an object type corresponding to one object identified in the reference satellite remote sensing subimage;
carrying out object contour extraction operation on the target satellite remote sensing sub-image to output a target object contour set, and carrying out object contour extraction operation on the reference satellite remote sensing sub-image to output a reference object contour set, wherein the target object contour set comprises a contour of each object extracted from the target satellite remote sensing sub-image, and the reference object contour set comprises a contour of each object extracted from the reference satellite remote sensing sub-image;
for each target object contour included in the target object contour set, performing contour similarity calculation on the target object contour and each reference object contour included in the reference object contour set, and performing mean value calculation operation on each contour similarity output by calculation to output a target contour similarity corresponding to the target object contour;
for each standard object type in a plurality of standard object types, performing statistical operation on the occurrence frequency of the standard object type in the target object type set to output a first occurrence frequency (namely the occurrence frequency of the standard object type) corresponding to the standard object type, and performing statistical operation on the occurrence frequency of the standard object type and the occurrence frequency in the reference object type set to output a second occurrence frequency corresponding to the standard object type;
for each standard object type in a plurality of standard object types, performing fusion operation on a first occurrence frequency and a second occurrence frequency corresponding to the standard object type according to the number of the object types included in the target object type set and the number of the object types included in the reference object type set to output a corresponding target occurrence frequency, and then performing positive correlation value determination operation according to the target occurrence frequency to output the type importance degree corresponding to the standard object type;
for each target object contour, performing an update operation on the target contour similarity corresponding to the target object contour according to the type importance degree corresponding to the object type corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour, and determining the type importance degree corresponding to the object type corresponding to the target object contour according to the minimum value of the type importance degrees corresponding to the standard object types under the condition that the object type corresponding to the target object contour does not belong to any standard object type in the standard object types;
and calculating and outputting the image similarity between the two frames of satellite remote sensing sub-images according to the updated contour similarity corresponding to each target object contour included in the target object contour set (for example, the average value of the updated contour similarities corresponding to each target object contour can be calculated).
In detail, in some possible implementation manners, the method includes the step of, for each standard object type in the plurality of standard object types, performing a fusion operation on the first frequency of occurrence and the second frequency of occurrence corresponding to the standard object type according to the number of object types included in the target object type set and the number of object types included in the reference object type set to output a corresponding target frequency of occurrence, and then performing a positive correlation value determination operation according to the target frequency of occurrence to output a type importance corresponding to the standard object type, which may further include the following specific contents:
calculating and outputting a first weighting coefficient corresponding to the first frequency of occurrence and a second weighting coefficient corresponding to the second frequency of occurrence according to the number of the object types included in the target object type set and the number of the object types included in the reference object type set (the first weighting coefficient is positively correlated to the number of the object types included in the target object type set, and the second weighting coefficient is positively correlated to the number of the object types included in the target reference object type set);
and for each standard object type in the plurality of standard object types, performing weighted mean calculation operation on the first occurrence frequency and the second occurrence frequency corresponding to the standard object type according to the first weighting coefficient and the second weighting coefficient to output target occurrence frequency corresponding to the standard object type, and then performing positive correlation value determination operation according to the target occurrence frequency to output type importance corresponding to the standard object type.
In detail, in some possible implementation manners, the step of updating the target contour similarity corresponding to the target object contour according to the type importance degree corresponding to the object type corresponding to the target object contour for each target object contour included in the above method to output the updated contour similarity corresponding to the target object contour further includes the following specific contents:
for each target object contour, determining whether an object type corresponding to the target object contour belongs to any standard object type in the plurality of standard object types;
for each target object contour, when the object type corresponding to the target object contour does not belong to any standard object type in the standard object types, marking the minimum value in the multiple types of importance corresponding to the standard object types as the type importance corresponding to the target object contour, and when the object type corresponding to the target object contour belongs to one standard object type in the standard object types, marking the type importance corresponding to the standard object type as the type importance corresponding to the target object contour;
and for each target object contour, performing product calculation operation on the type importance degree corresponding to the target object contour and the target contour similarity corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour.
In detail, in some possible implementation manners, in step S120 included in the foregoing method, the following details may be further included:
carrying out image marking operation on one satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a target satellite remote sensing sub-image, and carrying out image marking operation on the other satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a reference satellite remote sensing sub-image;
carrying out image difference operation on the target satellite remote sensing subimage and a pre-configured standard satellite remote sensing subimage to output a target difference image corresponding to the target satellite remote sensing subimage, and carrying out image difference operation on the reference satellite remote sensing subimage and the standard satellite remote sensing subimage to output a reference difference image corresponding to the reference satellite remote sensing subimage;
performing feature point identification operation (the feature point identification operation may include an ORB feature point identification technology) on the target satellite remote sensing sub-image to output a first feature point set corresponding to the target satellite remote sensing sub-image, and performing feature point identification operation on the target differential image to output a second feature point set corresponding to the target differential image;
performing feature point identification operation on the reference satellite remote sensing subimage to output a third feature point set corresponding to the reference satellite remote sensing subimage, and performing feature point identification operation on the reference differential image to output a fourth feature point set corresponding to the reference differential image;
according to a preset number and a pixel position corresponding to each feature point, performing clustering operation on a plurality of first feature points included in the first feature point set to output a plurality of first feature point subsets of which the number is the preset number, then according to the preset number and the pixel position corresponding to each feature point, performing clustering operation on a plurality of second feature points included in the second feature point set to output a plurality of second feature point subsets of which the number is the preset number, then according to the preset number and the pixel position corresponding to each feature point, performing clustering operation on a plurality of third feature points included in the third feature point set to output a plurality of third feature point subsets of which the number is the preset number, and then according to the preset number and the pixel position corresponding to each feature point, performing clustering operation on a plurality of fourth feature points included in the fourth feature point set, a plurality of fourth feature point subsets taking the output number as the preset number;
for each first feature point subset in the plurality of first feature point subsets, performing center position determination operation on the first feature point subset according to a pixel position corresponding to each first feature point included in the first feature point subset to output a first center position corresponding to the first feature point subset, and for each second feature point subset in the plurality of second feature point subsets, performing center position determination operation on the second feature point subset according to a pixel position corresponding to each second feature point included in the second feature point subset to output a second center position corresponding to the second feature point subset;
for each third feature point subset in the plurality of third feature point subsets, performing a center position determination operation on the third feature point subset according to a pixel position corresponding to each third feature point included in the third feature point subset to output a third center position corresponding to the third feature point subset, and for each fourth feature point subset in the plurality of fourth feature point subsets, performing a center position determination operation on the fourth feature point subset according to a pixel position corresponding to each fourth feature point included in the fourth feature point subset to output a fourth center position corresponding to the fourth feature point subset;
performing a one-to-one association operation on the plurality of first feature point subsets and the plurality of third feature point subsets according to a first central position corresponding to each first feature point subset and a third central position corresponding to each third feature point subset (that is, an average value of distances between the first central position and the third central position after association is minimum), and performing a one-to-one association operation on the plurality of second feature point subsets and the plurality of fourth feature point subsets according to a second central position corresponding to each second feature point subset and a fourth central position corresponding to each fourth feature point subset (that is, an average value of distances between the second central position and the fourth central position after association is minimum);
for each first feature point subset, performing a correlation calculation operation on the first feature point subset and the third feature point subset according to the pixel position of each first feature point included in the first feature point subset and the pixel position of each third feature point included in a third feature point subset associated with the first feature point subset (for example, an average value of distances between pixel positions may be used first, and then a negative correlation coefficient corresponding to the average value is used as a feature correlation), so as to output a feature correlation corresponding to the first feature point subset;
for each second feature point subset, performing a correlation calculation operation (as described above) on the second feature point subset and a fourth feature point subset associated with the second feature point subset according to a pixel position of each second feature point included in the second feature point subset and a pixel position of each fourth feature point included in the fourth feature point subset, so as to output a feature correlation corresponding to the second feature point subset;
and fusing to obtain the image similarity between the two frames of satellite remote sensing sub-images according to the feature correlation corresponding to each first feature point subset and the feature correlation corresponding to each second feature point subset (for example, a first average of the feature correlation corresponding to each first feature point subset is calculated first, a second average of the feature correlation corresponding to each second feature point subset is calculated, and the first average and the second average are subjected to weighted summation calculation).
In detail, in some possible implementation manners, in step S130 included in the foregoing method, the following details may be further included:
for each two frames of satellite remote sensing subimages in the multi-frame satellite remote sensing subimages, performing area distance calculation operation on the geographic areas corresponding to the two frames of satellite remote sensing subimages to output an area distance value between the two frames of satellite remote sensing subimages, and then determining an area correlation value between the two frames of satellite remote sensing subimages according to the area distance value between the two frames of satellite remote sensing subimages to output an area correlation value between the two frames of satellite remote sensing subimages (the area correlation value can be in negative correlation with the area distance value);
for each two frames of satellite remote sensing subimages in the multi-frame satellite remote sensing subimages, performing fusion operation on the area correlation value between the two frames of satellite remote sensing subimages and the image similarity between the two frames of satellite remote sensing subimages (for example, weighted mean calculation can be performed on the area correlation value and the image similarity) to output the image correlation value between the two frames of satellite remote sensing subimages;
and carrying out classified storage operation on the multiple frames of satellite remote sensing subimages according to the image correlation degree between every two frames of satellite remote sensing subimages so as to store the multiple frames of satellite remote sensing subimages.
In detail, in some possible implementation manners, the step of performing classified storage operation on the multiple frames of satellite remote sensing sub-images according to the image correlation between every two frames of satellite remote sensing sub-images to store the multiple frames of satellite remote sensing sub-images may further include the following specific contents:
step 1, carrying out classification operation on the multiframe satellite remote sensing subimages to form a plurality of remote sensing subimage initial sets corresponding to the multiframe satellite remote sensing subimages, wherein each remote sensing subimage initial set comprises at least one frame of satellite remote sensing subimage;
step 2, carrying out collection quantity statistics operation on the multiple remote sensing sub-image initial collections to output initial collection quantities corresponding to the multiple remote sensing sub-image initial collections, and then determining a first classification coefficient with a negative correlation relation according to the initial collection quantities;
step 3, for each initial set of remote sensing subimages in the multiple initial sets of remote sensing subimages, under the condition that the initial set of remote sensing subimages comprises one frame of satellite remote sensing subimages, marking the initial set of remote sensing subimages as a first initial set of remote sensing subimages, assigning the target image correlation degree corresponding to the first initial set of remote sensing subimages as the maximum image correlation degree, under the condition that the initial set of remote sensing subimages comprises at least two frames of satellite remote sensing subimages, carrying out size comparison operation on the image correlation degree between every two frames of satellite remote sensing subimages in the initial set of remote sensing subimages and a correlation reference value, and marking the initial set of remote sensing subimages as the first initial set of remote sensing subimages under the condition that the image correlation degree between every two frames of satellite remote sensing subimages in the initial set of remote sensing subimages is greater than or equal to the correlation reference value, marking the average value of the image correlation degrees between every two frames of satellite remote sensing sub-images included in the initial remote sensing sub-image set as the target image correlation degree corresponding to the first initial remote sensing sub-image set;
step 4, under the condition that at least one initial set of remote sensing sub-images in the multiple initial sets of remote sensing sub-images is not marked as a first initial set of remote sensing sub-images, skipping to the step 1, under the condition that each initial set of remote sensing sub-images in the multiple initial sets of remote sensing sub-images is marked as a first initial set of remote sensing sub-images, carrying out fusion operation on the correlation degree of a target image corresponding to each first initial set of remote sensing sub-images so as to output a second classification coefficient;
step 5, carrying out fusion operation on the first classification coefficient and the second classification coefficient to output a target classification coefficient, and then comparing the target classification coefficient with a classification coefficient reference value;
and 6, when the target classification coefficient is smaller than or equal to the classification coefficient reference value, skipping to step 1, when the target classification coefficient is larger than the classification coefficient reference value, marking the plurality of initial remote sensing sub-image sets formed by sequentially executing step 1 recently as a plurality of corresponding remote sensing sub-image sets, and then respectively storing each remote sensing sub-image set (for example, all satellite remote sensing sub-images included in each remote sensing sub-image set can be stored together for each remote sensing sub-image set, and the collective storage can mean that all satellite remote sensing sub-images are stored in one storage space of a plurality of storage spaces of one device or one storage device, or stored in different storage spaces, but respectively associated, so that the collective associated output can be conveniently realized during searching).
Referring to fig. 3, an embodiment of the present invention further provides a processing system for satellite remote sensing data, which can be applied to the cloud platform. The processing system of the satellite remote sensing data can comprise an image segmentation module, a similarity calculation module and an image classification storage module.
In detail, in some possible implementation manners, the image segmentation module is configured to perform a segmentation operation on a to-be-processed satellite remote sensing image according to a preset image segmentation size to output multiple frames of satellite remote sensing sub-images corresponding to the satellite remote sensing image, and the multiple frames of satellite remote sensing sub-images are spliced to form the satellite remote sensing image. The similarity calculation module is used for performing similarity calculation operation on every two frames of satellite remote sensing subimages in the multi-frame satellite remote sensing subimages to output image similarity between every two frames of satellite remote sensing subimages, and the image classification storage module is used for performing classification storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages to store the multi-frame satellite remote sensing subimages.
In summary, according to the method, the system and the cloud platform for processing the satellite remote sensing data provided by the invention, the to-be-processed satellite remote sensing image can be segmented according to the preset image segmentation size, so as to output the multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image. And performing similarity calculation operation on every two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images to output the image similarity between every two frames of satellite remote sensing sub-images. And carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages. Based on the foregoing, the satellite remote sensing subimages formed by segmentation can be combined with the image similarity between the satellite remote sensing subimages, so that after storage, search is facilitated (by classification, for example, the satellite remote sensing subimages with high image similarity are stored as one class, and the satellite remote sensing subimages with high image similarity are searched as a whole, or the satellite remote sensing subimages with low image similarity are searched as one class, so as to improve the diversity of the searched images), and the control effect on storage is improved to a certain extent, so that the problem of poor storage effect of the satellite remote sensing data in the prior art is solved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A processing method of satellite remote sensing data is characterized by being applied to a cloud platform and comprising the following steps:
according to a preset image segmentation size, carrying out segmentation operation on a satellite remote sensing image to be processed so as to output multi-frame satellite remote sensing subimages corresponding to the satellite remote sensing image, and splicing the multi-frame satellite remote sensing subimages to form the satellite remote sensing image;
performing similarity calculation operation on every two frames of satellite remote sensing subimages in the multiframe satellite remote sensing subimages to output the image similarity between every two frames of satellite remote sensing subimages;
and carrying out classified storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages.
2. The method for processing satellite remote sensing data according to claim 1, wherein the step of performing segmentation operation on the satellite remote sensing image to be processed according to the preset image segmentation size to output the multi-frame satellite remote sensing sub-images corresponding to the satellite remote sensing image comprises the following steps:
under the condition of receiving an original satellite remote sensing image, identifying whether a target storage request instruction for classified storage of the original satellite remote sensing image is received or not;
under the condition that a target storage request instruction for classified storage of the original satellite remote sensing image is identified and received, marking the original satellite remote sensing image as a satellite remote sensing image to be processed;
and according to a preset image segmentation size, performing segmentation operation on the satellite remote sensing image to be processed to output a plurality of frames of satellite remote sensing subimages corresponding to the satellite remote sensing image, wherein the image size of each frame of satellite remote sensing subimage is equal to the image segmentation size.
3. The method for processing satellite remote sensing data according to claim 2, wherein the step of marking the original satellite remote sensing image as the satellite remote sensing image to be processed in case that the target storage request instruction for classified storage of the original satellite remote sensing image is recognized to be received comprises:
under the condition that a target storage request instruction for classified storage of the original satellite remote sensing image is identified to be received, determining the image size of the original satellite remote sensing image so as to output the target image size corresponding to the original satellite remote sensing image;
comparing the size of the target image size with the size of the image size reference value;
and under the condition that the size of the target image is smaller than or equal to the image size reference value, determining not to mark the original satellite remote sensing image as a satellite remote sensing image to be processed, and under the condition that the size of the target image is larger than the image size reference value, marking the original satellite remote sensing image as the satellite remote sensing image to be processed.
4. The method for processing satellite remote sensing data according to claim 1, wherein the step of performing similarity calculation operation on every two frames of satellite remote sensing sub-images in the plurality of frames of satellite remote sensing sub-images to output image similarity between every two frames of satellite remote sensing sub-images comprises:
carrying out image marking operation on one satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a target satellite remote sensing sub-image, and carrying out image marking operation on the other satellite remote sensing sub-image in the two satellite remote sensing sub-images to form a reference satellite remote sensing sub-image;
performing object recognition operation on the target satellite remote sensing subimage to output a target object type set, and performing object recognition operation on the reference satellite remote sensing subimage to output a reference object type set, wherein each target object type included in the target object type set belongs to an object type corresponding to one object identified in the target satellite remote sensing subimage, and each reference object type included in the reference object type set belongs to an object type corresponding to one object identified in the reference satellite remote sensing subimage;
carrying out object contour extraction operation on the target satellite remote sensing sub-image to output a target object contour set, and carrying out object contour extraction operation on the reference satellite remote sensing sub-image to output a reference object contour set, wherein the target object contour set comprises a contour of each object extracted from the target satellite remote sensing sub-image, and the reference object contour set comprises a contour of each object extracted from the reference satellite remote sensing sub-image;
for each target object contour included in the target object contour set, performing contour similarity calculation on the target object contour and each reference object contour included in the reference object contour set, and performing mean value calculation operation on each contour similarity output by calculation to output a target contour similarity corresponding to the target object contour;
for each standard object type in a plurality of standard object types, carrying out statistical operation on the occurrence frequency of the standard object type in the target object type set to output a first occurrence frequency corresponding to the standard object type, and carrying out statistical operation on the occurrence frequency of the standard object type and the reference object type set to output a second occurrence frequency corresponding to the standard object type;
for each standard object type in a plurality of standard object types, performing fusion operation on a first occurrence frequency and a second occurrence frequency corresponding to the standard object type according to the number of the object types included in the target object type set and the number of the object types included in the reference object type set to output a corresponding target occurrence frequency, and then performing positive correlation value determination operation according to the target occurrence frequency to output the type importance corresponding to the standard object type;
for each target object contour, performing an updating operation on the target contour similarity corresponding to the target object contour according to the type importance degree corresponding to the object type corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour, and determining the type importance degree corresponding to the object type corresponding to the target object contour according to the minimum value of the type importance degrees corresponding to the standard object types when the object type corresponding to the target object contour does not belong to any standard object type of the standard object types;
and calculating and outputting the image similarity between the two frames of satellite remote sensing sub-images according to the updated contour similarity corresponding to each target object contour in the target object contour set.
5. The method for processing satellite remote sensing data according to claim 4, wherein for each standard object type of the plurality of standard object types, according to the number of object types included in the target object type set and the number of object types included in the reference object type set, the step of performing fusion operation on the first frequency of occurrence and the second frequency of occurrence corresponding to the standard object type to output the corresponding target frequency of occurrence, and then performing determination operation on positive correlation values according to the target frequency of occurrence to output the type importance corresponding to the standard object type comprises:
calculating and outputting a first weighting coefficient corresponding to the first frequency of occurrence and a second weighting coefficient corresponding to the second frequency of occurrence according to the number of the object types included in the target object type set and the number of the object types included in the reference object type set;
and for each standard object type in the plurality of standard object types, performing weighted mean calculation operation on the first occurrence frequency and the second occurrence frequency corresponding to the standard object type according to the first weighting coefficient and the second weighting coefficient to output target occurrence frequency corresponding to the standard object type, and then performing positive correlation value determination operation according to the target occurrence frequency to output type importance corresponding to the standard object type.
6. The method for processing satellite remote sensing data according to claim 4, wherein the step of updating the target contour similarity corresponding to the target object contour according to the type importance corresponding to the object type corresponding to the target object contour for each target object contour to output the updated contour similarity corresponding to the target object contour comprises:
for each target object contour, determining whether an object type corresponding to the target object contour belongs to any standard object type in the plurality of standard object types;
for each target object contour, when the object type corresponding to the target object contour does not belong to any standard object type in the standard object types, marking the minimum value in the multiple types of importance corresponding to the standard object types as the type importance corresponding to the target object contour, and when the object type corresponding to the target object contour belongs to one standard object type in the standard object types, marking the type importance corresponding to the standard object type as the type importance corresponding to the target object contour;
and for each target object contour, performing product calculation operation on the type importance degree corresponding to the target object contour and the target contour similarity corresponding to the target object contour to output the updated contour similarity corresponding to the target object contour.
7. The method for processing satellite remote sensing data according to claim 1, wherein the step of performing classified storage operation on the multiframe satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multiframe satellite remote sensing subimages comprises the following steps:
for each two frames of satellite remote sensing subimages in the multiple frames of satellite remote sensing subimages, performing area distance calculation operation on geographic areas corresponding to the two frames of satellite remote sensing subimages to output an area distance value between the two frames of satellite remote sensing subimages, and determining an area correlation value between the two frames of satellite remote sensing subimages according to the area distance value between the two frames of satellite remote sensing subimages to output the area correlation value between the two frames of satellite remote sensing subimages;
for each two frames of satellite remote sensing subimages in the multi-frame satellite remote sensing subimages, carrying out fusion operation on the area correlation value between the two frames of satellite remote sensing subimages and the image similarity between the two frames of satellite remote sensing subimages so as to output the image correlation value between the two frames of satellite remote sensing subimages;
and carrying out classified storage operation on the multiple frames of satellite remote sensing subimages according to the image correlation degree between every two frames of satellite remote sensing subimages so as to store the multiple frames of satellite remote sensing subimages.
8. The method for processing satellite remote sensing data according to claim 7, wherein the step of performing classified storage operation on the multiple frames of satellite remote sensing sub-images according to the image correlation degree between every two frames of satellite remote sensing sub-images to store the multiple frames of satellite remote sensing sub-images comprises the following steps:
step 1, carrying out classification operation on the multiframe satellite remote sensing subimages to form a plurality of remote sensing subimage initial sets corresponding to the multiframe satellite remote sensing subimages, wherein each remote sensing subimage initial set comprises at least one frame of satellite remote sensing subimage;
step 2, carrying out collection quantity statistics operation on the multiple remote sensing sub-image initial collections to output initial collection quantities corresponding to the multiple remote sensing sub-image initial collections, and then determining a first classification coefficient with a negative correlation relation according to the initial collection quantities;
step 3, for each remote sensing subimage initial set in the multiple remote sensing subimage initial sets, under the condition that the remote sensing subimage initial set comprises one frame of satellite remote sensing subimage, marking the remote sensing subimage initial set as a first remote sensing subimage initial set, assigning the target image correlation degree corresponding to the first remote sensing subimage initial set as the maximum image correlation degree, under the condition that the remote sensing subimage initial set comprises at least two frames of satellite remote sensing subimages, carrying out size comparison operation on the image correlation degree between each two frames of satellite remote sensing subimages included in the remote sensing subimage initial set and a correlation reference value, and marking the remote sensing subimage initial set as the first remote sensing subimage initial set under the condition that the image correlation degree between each two frames of satellite remote sensing subimages included in the remote sensing subimage initial set is greater than or equal to the correlation reference value, marking the average value of the image correlation degrees between every two frames of satellite remote sensing sub-images included in the initial remote sensing sub-image set as the target image correlation degree corresponding to the first initial remote sensing sub-image set;
step 4, under the condition that at least one initial set of remote sensing sub-images in the multiple initial sets of remote sensing sub-images is not marked as a first initial set of remote sensing sub-images, skipping to the step 1, under the condition that each initial set of remote sensing sub-images in the multiple initial sets of remote sensing sub-images is marked as a first initial set of remote sensing sub-images, carrying out fusion operation on the correlation degree of a target image corresponding to each first initial set of remote sensing sub-images so as to output a second classification coefficient;
step 5, carrying out fusion operation on the first classification coefficient and the second classification coefficient to output a target classification coefficient, and then comparing the target classification coefficient with a classification coefficient reference value;
and 6, under the condition that the target classification coefficient is smaller than or equal to the classification coefficient reference value, skipping to the step 1, under the condition that the target classification coefficient is larger than the classification coefficient reference value, marking the plurality of remote sensing sub-image initial sets formed by sequentially executing the step 1 as a plurality of corresponding remote sensing sub-image sets, and respectively storing each remote sensing sub-image set.
9. The processing system for the satellite remote sensing data is applied to a cloud platform and comprises the following components:
the image segmentation module is used for carrying out segmentation operation on a satellite remote sensing image to be processed according to a preset image segmentation size so as to output multi-frame satellite remote sensing subimages corresponding to the satellite remote sensing image, and the multi-frame satellite remote sensing subimages are spliced to form the satellite remote sensing image;
the similarity calculation module is used for performing similarity calculation operation on each two frames of satellite remote sensing sub-images in the multi-frame satellite remote sensing sub-images so as to output the image similarity between each two frames of satellite remote sensing sub-images;
and the image classification storage module is used for performing classification storage operation on the multi-frame satellite remote sensing subimages according to the image similarity between every two frames of satellite remote sensing subimages so as to store the multi-frame satellite remote sensing subimages.
10. A cloud platform, wherein the cloud platform is configured to execute the method for processing satellite remote sensing data according to any one of claims 1 to 8.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115826806A (en) * 2023-02-09 2023-03-21 广东粤港澳大湾区硬科技创新研究院 Auxiliary labeling method, device and system for satellite telemetry data

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102096816A (en) * 2011-01-28 2011-06-15 武汉大学 Multi-scale multi-level image segmentation method based on minimum spanning tree
CN105426903A (en) * 2015-10-27 2016-03-23 航天恒星科技有限公司 Cloud determination method and system for remote sensing satellite images
US20170124464A1 (en) * 2015-10-28 2017-05-04 Fractal Industries, Inc. Rapid predictive analysis of very large data sets using the distributed computational graph
CN106909644A (en) * 2017-02-21 2017-06-30 中国科学院电子学研究所 A kind of multistage tissue and indexing means towards mass remote sensing image
CN108255873A (en) * 2016-12-29 2018-07-06 广东中科遥感技术有限公司 Magnanimity remote sensing satellite automatic data processing method
CN109657616A (en) * 2018-12-19 2019-04-19 四川立维空间信息技术有限公司 A kind of remote sensing image land cover pattern automatic classification method
CN109657080A (en) * 2018-09-29 2019-04-19 中国科学院上海高等研究院 Distributed approach/the system and medium of high score satellite remote sensing date
CN111402299A (en) * 2020-04-08 2020-07-10 中国人民解放军海军航空大学 Remote sensing image target tracking method and device based on stationary orbit staring satellite
AU2021102192A4 (en) * 2021-04-26 2021-06-17 Qingdao Agricultural University Remote sensing classification and identification method, device, equipment and storage medium for soil types
CN113012094A (en) * 2020-11-30 2021-06-22 中国科学院空天信息创新研究院 Processing method of satellite remote sensing data and related device
CN113761240A (en) * 2021-09-30 2021-12-07 辽宁工程技术大学 Method and system for filing image data of stereo mapping satellite
CN114491107A (en) * 2021-12-21 2022-05-13 浙江大学 Layered block organization method based on five-layer fifteen-level remote sensing tile data
CN114494887A (en) * 2022-02-14 2022-05-13 中煤航测遥感集团有限公司 Remote sensing image classification method and device, computer equipment and readable storage medium

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102096816A (en) * 2011-01-28 2011-06-15 武汉大学 Multi-scale multi-level image segmentation method based on minimum spanning tree
CN105426903A (en) * 2015-10-27 2016-03-23 航天恒星科技有限公司 Cloud determination method and system for remote sensing satellite images
US20170124464A1 (en) * 2015-10-28 2017-05-04 Fractal Industries, Inc. Rapid predictive analysis of very large data sets using the distributed computational graph
CN108255873A (en) * 2016-12-29 2018-07-06 广东中科遥感技术有限公司 Magnanimity remote sensing satellite automatic data processing method
CN106909644A (en) * 2017-02-21 2017-06-30 中国科学院电子学研究所 A kind of multistage tissue and indexing means towards mass remote sensing image
CN109657080A (en) * 2018-09-29 2019-04-19 中国科学院上海高等研究院 Distributed approach/the system and medium of high score satellite remote sensing date
CN109657616A (en) * 2018-12-19 2019-04-19 四川立维空间信息技术有限公司 A kind of remote sensing image land cover pattern automatic classification method
CN111402299A (en) * 2020-04-08 2020-07-10 中国人民解放军海军航空大学 Remote sensing image target tracking method and device based on stationary orbit staring satellite
CN113012094A (en) * 2020-11-30 2021-06-22 中国科学院空天信息创新研究院 Processing method of satellite remote sensing data and related device
AU2021102192A4 (en) * 2021-04-26 2021-06-17 Qingdao Agricultural University Remote sensing classification and identification method, device, equipment and storage medium for soil types
CN113761240A (en) * 2021-09-30 2021-12-07 辽宁工程技术大学 Method and system for filing image data of stereo mapping satellite
CN114491107A (en) * 2021-12-21 2022-05-13 浙江大学 Layered block organization method based on five-layer fifteen-level remote sensing tile data
CN114494887A (en) * 2022-02-14 2022-05-13 中煤航测遥感集团有限公司 Remote sensing image classification method and device, computer equipment and readable storage medium

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
WEILI KOU ET AL: "HDFS Enabled Storage and Management of Remote Sensing Data", 《2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS》 *
王卓琳: "基于猫群算法的遥感影像数据存储优化方法研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 *
陆丽珍: "基于数据库方式的遥感图像库内容检索研究", 《中国博士学位论文全文数据库信息科技辑》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115826806A (en) * 2023-02-09 2023-03-21 广东粤港澳大湾区硬科技创新研究院 Auxiliary labeling method, device and system for satellite telemetry data

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