CN112287885A - Radiation normalization method and system - Google Patents
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Abstract
The invention provides a radiation normalization method and a system thereof, wherein the radiation normalization method comprises the following steps: selecting a region with stable earth surface state with relatively stable spectral characteristics according to specific image data; carrying out radiation distribution statistics on a plurality of time phase image data in the region with stable earth surface state to obtain the radiation distribution characteristics of each time phase image; and selecting one of the time phase images as a standard, and establishing and resolving equations for converting other time phase images to the standard through the radiation distribution characteristic values of a plurality of time phases. The radiation normalization method improves the detection precision of the change of the remote sensing image.
Description
Technical Field
The invention relates to the field of remote sensing image processing, in particular to a radiation normalization method and a radiation normalization system.
Background
The remote sensing image change detection is a technical method for understanding the earth surface change by using a remote sensing image, is one of important application directions of remote sensing, has the advantages of comprehensiveness, high efficiency and objectivity, and is widely applied to the fields of ecological environment protection, natural resource supervision, urban and rural construction investigation, agricultural production monitoring, natural disaster assessment and the like. The remote sensing image change detection is mainly to obtain the change condition of the surface in the area by analyzing, calculating and interpreting remote sensing images in the same area acquired at different times. However, due to the influence of factors such as seasons, illumination, atmosphere, sensors, and a platform, there are different degrees of radiation differences among multiple temporal remote sensing data, which causes the problem of spectrum difference between the same object and different objects, and causes a certain interference to change detection. The radiation correction is carried out on the multi-temporal data, the radiation difference caused by external factors is eliminated, the radiation normalization is realized, the method is an important preprocessing step before the change detection, and the improvement of the precision of the change detection is facilitated.
Scholars at home and abroad propose a plurality of radiation normalization correction methods, and the mainstream methods can be divided into two types: absolute radiation correction and relative radiation correction. Absolute radiation correction is the modeling of the physical process of imaging, converting the radiation gray value of the image pixels to the real reflectivity of the earth's surface. The result calculated by the method has definite physical significance, is suitable for quantitatively calculating various physical indexes, but the parameters required by the conversion process are not easy to obtain, so the practicability is not strong. The relative radiation correction does not aim at restoring the real earth surface reflectivity, but focuses on the radiation consistency or color consistency among images, so that the radiation of the same area in a plurality of images tends to be consistent as much as possible, and meanwhile, the difference characteristics among different earth surfaces are kept. The method does not need real-time parameters, is more convenient and practical, and has enough correction results for a large amount of qualitative analysis, thereby being widely applied.
Relative radiation correction can be divided into two categories again: relative radiometric correction based on pixel pairs and relative radiometric correction based on distribution. The relative radiation correction based on the pixel pair is to utilize unchanged pixel values to establish a conversion model in multi-time phase data, and then to use the model to correct the radiation value of the whole image. The method needs to select a pseudo-invariant pixel pair, a regression equation is determined by a least square method, and the processing effect is greatly influenced by the pixel pair extraction quality. And the distribution-based relative radiation correction is to adjust the pixel value according to the overall distribution characteristics of the image overlapping area. The method is simple and efficient, but the effect is not good under the conditions of large seasonal difference and large ground surface change. In actual production, image processing tools such as Photoshop are used to adjust the histogram of the image according to subjective visual perception, so that the color tone of the image tends to be uniform, which is often referred to as color matching processing. The method also belongs to relative radiation correction based on distribution, has good adjustment effect, completely depends on manual processing, can only be used for three-band data, and is difficult to meet the processing requirement of mass multi-source heterogeneous images.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
In view of this, the invention discloses a radiation normalization method and a system for implementing the radiation normalization method, so as to improve the detection accuracy of the remote sensing image change.
Specifically, the invention is realized by the following technical scheme:
in a first aspect, the invention discloses a radiation normalization method, which comprises the following steps:
selecting a region with stable earth surface state with relatively stable spectral characteristics according to specific image data;
carrying out radiation distribution statistics on a plurality of time phase image data in the region with stable earth surface state to obtain the radiation distribution characteristics of each time phase image;
and selecting one of the time phase images as a standard, and establishing and resolving equations for converting other time phase images to the standard through the radiation distribution characteristic values of a plurality of time phases.
In a second aspect, the present invention discloses a radiation normalization system, comprising:
selecting a module: the method comprises the steps of selecting a stable region of the earth surface with relatively stable spectral characteristics according to specific image data;
a calculation module: the radiation distribution statistics is carried out on a plurality of time phase image data in the stable earth surface state area, and the radiation distribution characteristics of each time phase image are obtained;
a conversion module: and selecting one of the time phase images as a standard, and establishing and resolving equations for converting other time phase images to the standard through the radiation distribution characteristic values of a plurality of time phases.
In a third aspect, the invention discloses a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the radiation normalization method according to the first aspect.
In a fourth aspect, the present invention discloses a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the radiation normalization method according to the first aspect when executing the program.
The invention provides a radiation normalization method and a system thereof, which belong to the category of distributed relative radiation correction from the specific field division, but the invention is improved on the basis of the original relative radiation method as follows: firstly, from the spatial distribution dimension, the limitation of the earth surface stable area is increased, only the relative radiation characteristic extraction is carried out on multi-temporal image data in the stable area range, the workload is reduced, the efficiency is improved, certain specific method steps are given to the screening method of the earth surface stable area, secondly, from the data distribution dimension, the distribution proportion limitation is increased, only a main body value with larger distribution proportion is selected for carrying out characteristic operation, through the screening of the two dimensions, the influences of external change factors such as land type change, seasonal change, illumination change and the like on the radiation distribution characteristic can be eliminated, the interference of non-main body values on the radiation distribution characteristic is also eliminated, and therefore, the final correction result can better recover the real spectral characteristic and the texture characteristic of the earth surface. In a word, the method has wide adaptability for calculating the speed block, can be used for relative radiation correction of various visible light remote sensing image data, eliminates the phenomenon of homomorphism and heteromorphism caused by external factors among multi-time-phase images, improves the detection precision of remote sensing image change, and has good effect.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart of a radiation normalization method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a radiation normalization method according to another embodiment of the present invention;
FIG. 3 is a standard image and histogram thereof according to an embodiment of the present invention;
FIG. 4 is a diagram of an image before correction and a histogram thereof according to an embodiment of the present invention;
FIG. 5 is a diagram of a corrected image and a histogram thereof according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The invention discloses a radiation normalization method, which comprises the following steps:
selecting a region with stable earth surface state with relatively stable spectral characteristics according to specific image data;
carrying out radiation distribution statistics on a plurality of time phase image data in the region with stable earth surface state to obtain the radiation distribution characteristics of each time phase image;
and selecting one of the time phase images as a standard, and establishing and resolving equations for converting other time phase images to the standard through the radiation distribution characteristic values of a plurality of time phases.
The invention provides a method for improving the detection precision of remote sensing image change in order to solve the problem that the radiation correction processing in the prior art is poor in effect under the conditions that the image is greatly influenced by pixels on the extraction quality, or the seasonal difference is large and the earth surface changes are large, or the radiation correction processing completely depends on manual processing and can only be used for three-band data, so that the processing requirement of massive multi-source heterogeneous images is difficult to meet.
Fig. 1 is a schematic flow chart of a radiation normalization method disclosed in an embodiment of the present invention, and referring to fig. 1, the method includes the following steps:
s1, selecting a region with stable ground surface state having relatively stable spectral characteristics according to the specific image data.
In the step, the earth surface which is easy to change along with seasons and the earth surface which is not easy to change along with the seasons are distinguished, and an area with stable earth surface state is selected. The spectrum of vegetation type ground surfaces, such as mountainous regions, grasslands and farmlands, is easy to change along with the change of seasons, and the ground surfaces of buildings, roads, squares, water bodies, evergreen vegetation and the like have relatively stable spectral characteristics in any seasons. This step may be performed by any one of the following four methods as appropriate according to the image data:
preferably, the method for selecting the stable region of the surface state comprises the following steps: and when the area history vector data exists, the area history vector data is obtained by editing and modifying the existing vector data. This method is the most fast but requires auxiliary data, which is in most cases difficult to achieve.
Preferably, the method for selecting the stable region of the surface state comprises the following steps: and judging a plurality of time phase images through direct visual observation, and drawing a stable and unchangeable ground surface area for a long time from the time phase images. The method has more manual participation and is relatively time-consuming and labor-consuming.
Preferably, the method for selecting the stable region of the surface state comprises the following steps: a small number of samples are outlined first, and then the appropriate classifier is selected to extract the required block.
The stable regions extracted by the three methods are vector data, and a general algorithm of vector to grid is adopted to manufacture mask data. The side length of the grid of the mask must be consistent with the image to be corrected, so that each grid in the mask can correspond to each pixel in the image one by one. In the subsequent operation, whether each pixel takes into account the distribution statistics can be judged through the mask grid numerical value.
Preferably, the method for selecting the stable region of the surface state comprises the following steps: the method comprises the following steps of calculating differences between every two N time phase images to obtain N-1 difference graphs, normalizing and summing the difference graphs to obtain a difference accumulation graph, setting a certain threshold value for the difference accumulation graph, and marking the difference which is lower than the threshold value as a stable region W of the earth surface state, wherein the specific calculation process refers to the following formula:
Di＝Pi-Pi-1
wherein P represents a multi-temporal image, D represents a difference image of two images, S is an accumulation image obtained by normalizing and summing all the difference images, and S isc，rThe value representing the row r and column c of the accumulation map, W is the stable region mark obtained when the accumulation value takes the threshold value delta, namely the mask.
In practice, the fourth method is used in most cases to automatically extract the stable region by a calculation formula.
S2, carrying out radiation distribution statistics on a plurality of time phase image data in the stable earth surface area to obtain the radiation distribution characteristics of each time phase image;
in the step S2, the radiation distribution features are a set of values used to describe the overall distribution features of the pixel values of the remote sensing data, the most common features are mean, median, standard deviation, variance, and peak and extreme values of the distribution histogram may also be used. Each wave band of the image can extract a group of radiation distribution characteristic values which describe the basic characteristics of the wave band numerical distribution. For a panchromatic image, only one group of radiation distribution characteristic values are calculated, and for a multispectral or even hyperspectral image, the radiation distribution characteristic values are required to be calculated for each wave band.
Due to the complex and various earth surface states, the distribution of image values is often dispersed, and a small number of abnormal regions may have abnormal values to generate interference on global radiation characteristics. Therefore, non-subject values are excluded, so that the final correction result can better reflect the real spectral features and texture features of the earth surface.
The specific calculation method comprises the following steps:
s21, calculating an image radiation distribution array: for integer type image values, calculating the number of pixels according to the value length, for non-integer type image values, performing statistics after rounding an initial value, and setting a mask, wherein the statistics only aims at the pixels in the stable region of the earth surface, and the specific formula is as follows:
wherein W is the mask, and in the above formula of each Array, the element ArraykThe number of pixels with the radiation value equal to k in the stable area is represented, and the sum of all elements of the Array is necessarily smaller than the number of pixels of the image due to the exclusion of a part of change areas;
s22, excluding non-subject values: firstly, determining the pixel ratio to be extracted, and selecting the main body pixel Percent with larger distribution ratio according to the pixel ratiokPerforming characteristic operation to generate a new Arrayc；
S23, calculating a distribution characteristic value: using a new ArraycAnd calculating array characteristic values.
And S3, selecting one of the time phase images as a standard, and establishing and resolving an equation for converting other time phase images into the standard through a plurality of time phase radiation distribution characteristic values.
Preferably, in this step, an equation for converting the other phase images P into the standard is established and solved through a mean value mean and a standard deviation std in the radiation distribution characteristic values, and a specific solving process is performed according to the following equation:
Qi，j＝(Pi，j-(meanQ-meanp))(stdQ/stdP)。
in a word, the method fundamentally improves the detection precision of the remote sensing image change, is suitable for wide application, and is explained by specific practical cases:
1) remote sensing images of local plain areas of Jiangsu province in 2019 and 2020 are used, the resolution is 0.8 m and the images comprise three wave bands of red, green and blue. Observing the original image (fig. 2 and 3) shows that: the data in 2020 is relatively close to the real ground surface and basically has no color cast, while the green hue of the data in 2019 is obviously deficient and the whole color is not real enough. Due to the difference of the illumination angle and the illumination intensity, the shadow area and the light reflection degree of the data of two years are also obviously different. In addition, because the two images are taken at a time interval of one year, the ground surface changes greatly, farmland area changes are particularly obvious, some bare lands become crops, and some crops become bare lands. From the histogram distribution of the original image, it can also be seen that there is a large radiation difference between the two phase images.
2) Selecting a stable region of the earth's surface
And manually drawing stable areas including buildings, roads, greenbelts and the like which are not changed, and manufacturing a mask.
3) Calculating radiation distribution characteristics in a stable region
And in the coverage range of the stable region, carrying out radiation distribution statistics on the image data of the two time phases to obtain the radiation distribution characteristics of each image in the stable region.
The specific operation is as follows:
firstly, counting an image radiation distribution array. For an image with a bit depth of 8 values and a type of fluid, the array length is 28I.e., 256 elements, respectively, the number of pixels having a value equal to 0 to 255 is recorded. For images with different bit depths, array records with different sizes are adopted. If the image value is of a non-integer type such as float, statistics is needed after the initial value is rounded. The statistics are only for pixels within the stable region due to the mask being set. W is the mask data, each element Array in the ArraykAll represent the number of pixels with the radiation value equal to k in the stable area, and the sum of all elements of the Array is necessarily smaller than the number of pixels of the image because a part of the change area is excluded.
Then, excluding non-subject values, determining the pixel proportion to be extracted, which can be generally set to 0.1% -1%, that is, selecting the subject pixels with larger distribution proportion to perform characteristic operation, wherein the pixels with the distribution proportion larger than 0.1% are selected to participate in the operation. Get Percent immediatelykElements greater than 0.1% in the Array, and generating a new Arrayc。
Finally, the distribution characteristic value is calculated. Using ArraycAnd calculating an array characteristic value, generally selecting a mean value mean and a standard deviation std, and if the image distribution is special and the deviation from the normal distribution is large, calculating a median mid and an extreme value max/min, and supplementing and correcting the characteristic value.
4) Establish conversion equations and solve
Selecting the 2020 image as a standard, establishing an equation for the radiation conversion from the 2019 image P to the 2020 image through radiation distribution characteristic values mean and std of the 2020 and 2019 images, and calculating to obtain the 2019 corrected image. Therefore, all images are unified to the same distribution characteristic, and the relative radiation correction is completed.
Qi，j＝(Pi，j-(meanQ-meanp))(stdQ/stdP)。
5) Performing radiation normalization correction
The final result is shown in fig. 4. It can be seen that after the image is corrected by the method based on the stable region, the whole color is closer to the reference image, the correction effect is good, and the subsequent interpretation analysis and change detection of the image are facilitated.
Fig. 5 is a schematic structural diagram of a radiation normalization system disclosed by the invention, which comprises:
the selection module 101: the method comprises the steps of selecting a stable region of the earth surface with relatively stable spectral characteristics according to specific image data;
the calculation module 102: the radiation distribution statistics is carried out on a plurality of time phase image data in the stable earth surface state area, and the radiation distribution characteristics of each time phase image are obtained;
the conversion module 103: and the method is used for selecting one of the phase images as a standard, establishing an equation for converting the other phase images to the standard through the radiation distribution characteristic values of a plurality of phases and calculating.
The radiation normalization system mainly comprises the three modules, so that the relative radiation of the change of the remote sensing image can be accurately corrected, the precision is high, and the application is wide.
In specific implementation, the above modules may be implemented as independent entities, or may be combined arbitrarily to be implemented as the same or several entities, and specific implementation of the above units may refer to the foregoing method embodiments, which are not described herein again.
Fig. 6 is a schematic structural diagram of a computer device disclosed by the invention. Referring to fig. 6, the computer apparatus includes: an input device 63, an output device 64, a memory 62 and a processor 61; the memory 62 for storing one or more programs; when executed by the one or more processors 61, cause the one or more processors 61 to implement a method of radiation normalization as provided in the embodiments above; wherein the input device 63, the output device 64, the memory 62 and the processor 61 may be connected by a bus or other means, as exemplified by the bus connection in fig. 6.
The memory 62 is a computer readable and writable storage medium, and can be used for storing software programs, computer executable programs, and program instructions corresponding to a radiation normalization method according to an embodiment of the present application; the memory 62 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the device, and the like; further, the memory 62 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device; in some examples, the memory 62 may further include memory located remotely from the processor 61, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 63 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function control of the apparatus; the output device 64 may include a display device such as a display screen.
The processor 61 executes various functional applications of the device and data processing by executing software programs, instructions, and modules stored in the memory 62.
The computer device provided above can be used to execute the radiation normalization method provided in the above embodiments, and has corresponding functions and advantages.
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method of radiation normalization as provided in the above embodiments, the storage medium being any of various types of memory devices or storage devices, the storage medium including: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc.; the storage medium may also include other types of memory or combinations thereof; in addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet); the second computer system may provide program instructions to the first computer for execution. A storage medium includes two or more storage media that may reside in different locations, such as in different computer systems connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the radiation normalization method described in the above embodiments, and may also perform related operations in a radiation normalization method provided in any embodiments of the present application.
Finally, it should be noted that: while this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In other instances, features described in connection with one embodiment may be implemented as discrete components or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Further, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
The above description is only exemplary of the present disclosure and should not be taken as limiting the disclosure, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A method of radiation normalization, comprising the steps of:
selecting a region with stable earth surface state with relatively stable spectral characteristics according to specific image data;
carrying out radiation distribution statistics on a plurality of time phase image data in the region with stable earth surface state to obtain the radiation distribution characteristics of each time phase image;
and selecting one of the time phase images as a standard, and establishing and resolving equations for converting other time phase images to the standard through a plurality of time phase radiation distribution characteristic values.
2. The radiation normalization method of claim 1, wherein the method of selecting a stable region of surface conditions comprises: and when the area history vector data exists, the area history vector data is obtained by editing and modifying the existing vector data.
3. The radiation normalization method of claim 1, wherein the method of selecting a stable region of surface conditions comprises: and judging a plurality of time phase images through direct visual observation, and drawing a stable and unchangeable ground surface area for a long time from the time phase images.
4. The radiation normalization method of claim 1, wherein the method of selecting a stable region of surface conditions comprises: a small number of samples are outlined first, and then the appropriate classifier is selected to extract the required block.
5. The radiation normalization method of claim 1, wherein the method of selecting a stable region of surface conditions comprises: the method comprises the following steps of calculating differences between every two N time phase images to obtain N-1 difference graphs, normalizing and summing the difference graphs to obtain a difference accumulation graph, setting a certain threshold value for the difference accumulation graph, and marking the difference which is lower than the threshold value as a stable region W of the earth surface state, wherein the specific calculation process refers to the following formula:
Di＝Pi-Pi-1
wherein P represents a multi-temporal image, D represents a difference image of two images, S is an accumulation image obtained by normalizing and summing all the difference images, and S isc，rThe value representing the row r and column c of the accumulation map, W is the stable region mark obtained when the accumulation value takes the threshold value delta, namely the mask.
6. The radiation normalization method of claim 5, wherein the calculation method of the radiation distribution characteristics comprises:
calculating an image radiation distribution array: for integer type image values, calculating the number of pixels according to the value length, for non-integer type image values, performing statistics after rounding an initial value, and setting a mask, wherein the statistics only aims at the pixels in the stable region of the earth surface, and the specific formula is as follows:
wherein W is the mask, and in the above formula of each Array, the element ArraykThe number of pixels with the radiation value equal to k in the stable area is represented, and the sum of all elements of the Array is necessarily smaller than the number of pixels of the image due to the exclusion of a part of change areas;
exclusion of non-subject values: firstly, determining the pixel ratio to be extracted, and selecting the main body pixel Percent with larger distribution ratio according to the pixel ratiokPerforming characteristic operation to generate a new Arrayc；
Calculating a distribution characteristic value: using a new ArraycAnd calculating array characteristic values.
7. The radiation normalization method according to claim 1, wherein equations for converting other phase images P to the standard are established and solved according to a mean and a standard deviation std in the radiation distribution characteristic values, and a specific solving process is performed according to the following equations:
Qi，j＝(Pi，j-(meanQ-meanp))(stdQ/stdP)。
8. a radiation normalization system, comprising:
selecting a module: the method comprises the steps of selecting a stable region of the earth surface with relatively stable spectral characteristics according to specific image data;
a calculation module: the radiation distribution statistics is carried out on a plurality of time phase image data in the stable earth surface state area, and the radiation distribution characteristics of each time phase image are obtained;
a conversion module: and the method is used for selecting one of the phase images as a standard, establishing an equation for converting the other phase images to the standard through the radiation distribution characteristic values of a plurality of phases and calculating.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the radiation normalization method of any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, carries out the steps of the radiation normalization method according to any of claims 1-7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009158563A1 (en) * | 2008-06-26 | 2009-12-30 | Spectrum Health | Method for treating or preventing radiation damage by in vivo gene therapy |
| CN101777174A (en) * | 2009-12-30 | 2010-07-14 | 深圳先进技术研究院 | Relative radiation normalization method for multi-temporal remote sensing image |
| CN108731817A (en) * | 2018-05-31 | 2018-11-02 | 中南林业科技大学 | The different sensors infra-red radiation normalizing modeling method differentiated applied to forest fires hot spot |
| CN109086661A (en) * | 2018-06-14 | 2018-12-25 | 中科禾信遥感科技（苏州）有限公司 | A kind of crops relative radiometric normalization method and device |
| CN109215038A (en) * | 2018-09-29 | 2019-01-15 | 中国资源卫星应用中心 | A kind of intelligent information retrieval method and system based on remote sensing image |
| CN111178304A (en) * | 2019-12-31 | 2020-05-19 | 江苏省测绘研究所 | High-resolution remote sensing image pixel level interpretation method based on full convolution neural network |
-
2020
- 2020-11-20 CN CN202011306156.4A patent/CN112287885B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009158563A1 (en) * | 2008-06-26 | 2009-12-30 | Spectrum Health | Method for treating or preventing radiation damage by in vivo gene therapy |
| CN101777174A (en) * | 2009-12-30 | 2010-07-14 | 深圳先进技术研究院 | Relative radiation normalization method for multi-temporal remote sensing image |
| CN108731817A (en) * | 2018-05-31 | 2018-11-02 | 中南林业科技大学 | The different sensors infra-red radiation normalizing modeling method differentiated applied to forest fires hot spot |
| CN109086661A (en) * | 2018-06-14 | 2018-12-25 | 中科禾信遥感科技（苏州）有限公司 | A kind of crops relative radiometric normalization method and device |
| CN109215038A (en) * | 2018-09-29 | 2019-01-15 | 中国资源卫星应用中心 | A kind of intelligent information retrieval method and system based on remote sensing image |
| CN111178304A (en) * | 2019-12-31 | 2020-05-19 | 江苏省测绘研究所 | High-resolution remote sensing image pixel level interpretation method based on full convolution neural network |
Non-Patent Citations (4)
| Title |
|---|
| LI.HW等: "Kcca-based Radiation Normalization Method for Hyperspectral Remote Sensing Images", 《AOPC 2019: OPTICAL SENSING AND IMAGING TECHNOLOGY》 * |
| LIJUAN SHI等: "Atmospheric influences analysis in passive microwave remote sensing", 《2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)》 * |
| 李尚洁: "多时相跨传感器遥感影像的相对辐射归一化方法研究", 《中国优秀硕士学位论文全文数据库农业科技辑》 * |
| 黄敏: "基于掩膜的高光谱遥感目标地物特征提取", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
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