CN112166694B - Method for inlaying and making image in small satellite region - Google Patents
Method for inlaying and making image in small satellite region Download PDFInfo
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Abstract
The invention belongs to the field of remote sensing digital image processing, and discloses a method for inlaying and manufacturing a microsatellite region image map, which comprises the following steps: 1) acquiring multispectral image data of the small satellite; 2) preprocessing multispectral image data of the small satellite; 3) orthorectifying multispectral images of the small satellites; 4) automatically blocking and cataloguing multispectral ortho-images of the small satellite; 5) color homogenizing and sub-area embedding; 6) simulating color conversion; 7) seamlessly embedding the sub-regions; 8) and (6) drawing. The invention establishes the method for embedding and manufacturing the multispectral regional image map of the microsatellite aiming at the characteristics of wide coverage and high revisit of the microsatellite, realizes the high-precision and rapid manufacturing of the image embedded product in the middle and high resolution region, and can provide a remote sensing image product with strong spot for domestic scientific research and industrial application.
Description
Technical Field
The invention belongs to the field of remote sensing digital image processing, and relates to a method for inlaying and manufacturing a satellite data area image map with medium resolution.
Background
With the development of space technology, optical sensors, remote sensing applications and computer technology, earth observation systems constructed by optical remote sensing satellites or small satellite constellations have become the main direction of global earth observation development. Meanwhile, with the continuous popularization and deepening of remote sensing application in various industries in China, the requirement for satellite image data with medium and high resolution is increasingly strong, and the method is particularly suitable for important application fields such as large-area resource investigation and the like. There is a contradiction between remote sensing data and regional applications, mainly expressed as:
(1) due to specific imaging characteristics (orbit, breadth and the like) of the satellite, the integrity requirement of a research area is often difficult to meet by one-time imaging of the satellite, and the mosaic processing of the area satellite images is required.
(2) With the continuous popularization and deepening of remote sensing application in various industries in China, the requirements of regional application on the timeliness and the sequence of the mosaic image are higher and higher, and a set of quick and standardized manufacturing method and flow are urgently needed.
(3) At present, China also makes national mosaic by using foreign satellite data (such as MSS, TM and the like) and Chinese resource satellite data, but the making period is long, the timeliness is poor, and the making process and the method have pertinence,
the method is difficult to adapt to the characteristics of wide coverage and high revisit of the data of the microsatellite.
Based on the analysis, a set of complete image mosaic and rapid manufacturing method based on the data characteristics and the regional characteristics of the microsatellite is still lacked aiming at the microsatellite data.
Disclosure of Invention
The invention aims to provide a method for inlaying and manufacturing a small satellite area image map, which realizes high-precision and quick manufacturing of a medium-high resolution area image inlaying product.
In order to achieve the above object, the method for inlaying and manufacturing the image map of the minisatellite region comprises the following steps:
the method comprises the following steps: obtaining microsatellite multispectral image data
Step two: microsatellite multispectral image data preprocessing
Preprocessing each orbit data to obtain a moonlet multispectral radiation correction image product;
step three: microsatellite multispectral image ortho-rectification
The method adopts a general push-broom model to carry out orthorectification processing on the multispectral image, and comprises the following implementation steps:
(1) collecting reference data
(2) Establishing an image orthorectification model
(3) Ortho image sampling output
(4) Evaluating the plane positioning precision of the output result of the orthoimage;
step four: automatic blocking and cataloguing of multispectral ortho-images of minisatellites
Dividing the mosaic area into a plurality of sub-areas based on the geographic space characteristics, the data characteristics of the microsatellite, the software and hardware bearing capacity and the processing speed of the mosaic area, and automatically cataloguing each sub-area; then, cutting the multispectral ortho-image of the small satellite according to the range of the sub-region, and respectively storing the cut image files into corresponding sub-region directories; meanwhile, each directory also comprises a coordinate file;
step five: color uniformity and subregion tessellation
Taking the subareas as processing units, and firstly, respectively carrying out uniform color processing and embedding on each subarea to obtain subarea image data with consistent color tones; then, adjusting the colors among the sub-regions by adopting a method of joint tone among the sub-regions to realize uniform tone of images of all the sub-regions;
step six: simulated color conversion
Carrying out simulated color conversion on the microsatellite false color data subjected to color homogenizing treatment to generate a simulated color product;
step seven: seamless tessellation between sub-regions
Seamlessly embedding all the sub-region files by using the geographic coordinate information of the sub-region files;
step eight: drawing
And (4) making various regional image maps with various scales according to the designed scales and the requirements of picture finishing according to related drawing standards.
Preferably, the mosaic area is a china land area, when the multispectral ortho-image of the small satellite is automatically partitioned, the china land territory area is partitioned into 10 × 8 sub-areas, each sub-area is 17125 × 17700 pixels from east to west, 1000 pixels are overlapped among the sub-areas, and 53 sub-areas actually covering the china land area are provided.
Preferably, when the multispectral ortho-image of the small satellite is automatically cataloged, the cataloging method comprises the following steps: and automatically cataloging the sub-regions according to the sequence from top to bottom and from left to right, wherein the sub-regions are subjected to the step of 'C + column number + R + row number'.
Preferably, in step five, the overlapping part of the data in the same sub-area is adjusted by using a feathering method.
Preferably, in the fifth step, the inter-sub-region joint tone method uses one sub-region image as a tone reference, performs tone transmission by using an overlapping region between sub-regions, adjusts four adjacent images, which are recorded as A, B, C, D, then adjusts the adjacent images by using A, B, C, D as a reference, and so on, and finally achieves uniform tone of the whole region.
Preferably, when the color conversion is simulated in the sixth step, the conversion formula is as follows:
red natural color 0.40 × false color near red band +0.60 × false color red band +0.10 × false color green band
Green natural color 0.65 near red band-0.12 near red band +0.60 near green band
Blue natural color is 0.0 × false color near red band +0.20 × false color red band +0.90 × false color green band.
Preferably, when the control points in the step three are selected, the error in the control points meets the following requirements: the error in the control point is within the range of 0.5, 1.5 for plain and hilly land, and within the range of 1.5, 2 for mountain and mountain land.
Preferably, the detection result of the positioning accuracy of the plane in the third step meets the following requirements: for plain and hilly lands, the plane positioning precision is less than or equal to 125 meters; for mountain land and high mountain land, the plane positioning precision is less than or equal to 185 meters.
The invention aims at the characteristics of wide coverage and high revisit of the small satellite, establishes the mosaic and manufacturing method of the multispectral regional image of the small satellite, and can quickly manufacture the mosaic product of the regional image with medium and high resolution at high precision based on the method. The mosaic product obtained based on the research of the microsatellite can be used for the research of finer ground surface conditions in a large-scale area, and a new remote sensing image product is provided for domestic scientific research and industrial application; providing a stable remote sensing data source for timely updating of small-scale topographic maps and building of a basic space database in China; the method is helpful to promote the popularization and application of the data of the microsatellite, and promote the development of digital earth science and technology in China and the application and popularization of remote sensing and earth information science in China.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an imaging model of a Beijing I microsatellite multispectral imager;
FIG. 3 is a flowchart of an orthorectification process of multispectral images of a microsatellite;
FIG. 4 is a schematic diagram of Chinese land area data partitioning and cataloging;
FIG. 5 is a schematic diagram of automatic blocking and cataloging of a microsatellite multi-spectral ortho image;
FIG. 6 is a schematic view of inter-domain diffusion coherent modulation of large domain sub-domains;
FIG. 7 is a national mosaic of images of Beijing I microsatellite produced by the present invention.
Detailed Description
The invention will be described in detail with reference to the drawings by taking the inlaying and manufacturing method of the Beijing I minisatellite region image map as an example.
FIG. 1 shows a microsatellite national image mosaicing and production flow diagram comprising the steps of:
the method comprises the following steps: microsatellite multispectral image data acquisition
By referring to meteorological satellite cloud pictures and MODIS satellite data, the small satellite shooting task and the data downloading task in the national land area are flexibly regulated and controlled, so that the multispectral data of the small satellite can be efficiently obtained. The method for acquiring the multispectral image data of the satellite refers to a patent of 'a selective downloading method for realizing optical remote sensing satellite data based on a satellite cloud picture', and the patent number is 200810077947.7.
Step two: microsatellite multispectral image data preprocessing
And finishing strip removal, wave band registration, MTF restoration and other processing on each rail of data to obtain a multispectral radiation correction image product of the Beijing I minisatellite.
The stripe noise is commonly existing in imaging of satellite-borne, airborne multi-sensor and single-sensor spectrometers, and is a special noise which has certain periodicity and directivity and is distributed in a stripe shape in an image. The noise is mainly caused by the response difference of each CCD, and the complex circuit, mechanical movement, thermal environment and the like of the device, so that the phenomenon that the gray scales of the adjacent ground objects are very different is caused. The banding noise can be removed by adopting a statistical method, a wavelet method and the like.
(1) The statistical method is mainly used for removing the stripe noise through statistical information of image characteristics, and comprises a histogram matching method, a moment matching method and the like.
The histogram matching method assumes that the sub-images acquired by each detection element of the sensor have the same probability density function distribution, and adjusts the gray level distribution of the sub-images acquired by each sensor to the same form.
The moment matching method assumes that the ground features observed by each detecting element of the sensor have the same radiation distribution mode, the change of the recorded data and the gain offset of radiation correction are in a linear relation, and the purpose of removing strips is achieved by adjusting the mean variance of the sub-images obtained by each detecting element to achieve a certain reference value.
(2) The wavelet method removes noise by studying the reflection of the banding noise in each component of the wavelet transform. The wavelet transform has the characteristic of time-frequency localization analysis, better solves the contradiction of Fourier transform in a space domain and a frequency domain, can simultaneously have good localization characteristic in the frequency domain and the space domain, and better separates the strip noise energy. The wavelet transformation for removing the band comprises the processes of wavelet base selection, wavelet transformation, component processing, inverse transformation and the like.
Aiming at the nonlinear geometric distortion among three bands of the multispectral image of the small satellite, the invention realizes the automatic registration among the bands by using an image gray level cross correlation registration method. The specific method and the specific process are referred to in Chengzheng Chao, Rowinfii and other articles of the Beijing I microsatellite multispectral image wave band registration and image deformation evaluation (remote sensing science and report, 2006-05).
Since the acquisition of the remote sensing image is a complex process, the remote sensing image is necessarily subjected to attenuation caused by air passing and returning hundreds of kilometers in front of an entrance pupil and the flying motion of a satellite-borne optical system, a sensor, an electronic circuit and a satellite after the entrance pupil, so that the inherent texture and geometric characteristics of the image have a remarkable fading phenomenon, and the attenuation can be expressed by MTF. And performing MTF compensation and recovery on the multispectral images of the small satellites by adopting wiener filtering according to the inverse process of satellite remote sensing imaging. The specific algorithm process is described in the article of Li Shengyang, Zhu Qiangguang research on MTF analysis of DMC satellite images and recovery method thereof (report of remote sensing, 2005, 04 th).
Step three: microsatellite multispectral image ortho-rectification
Fig. 3 shows a small satellite multispectral image ortho-rectification flowchart, which includes the following steps:
(1) collecting reference data
A1: 10 ten thousand topography maps, field checkpoints and an ASTGTM 30 meter DEM (digital elevation model) were acquired.
(2) Establishing an image orthorectification model
The method is characterized in that a general push-broom model is adopted to carry out orthorectification processing on a multispectral image, a collinear equation correction method is adopted for the push-broom model, ground elevation information is introduced by setting a mathematical basis and satellite parameters, control points are selected on a topographic map to establish a transformation relation between an image coordinate and a ground coordinate, and the geometric form of an imaging space is directly described, so that high-precision image orthorectification is realized. The mathematical basis, parameter setting, control point and digital elevation model selection method of the push-broom model is as follows:
a) mathematics foundation
The mathematical basis of the push-broom model adopted by the invention is as follows:
project type: albers cosmetic Equal Area
Spheroid name (ellipsoid): krasovsky
Datum name (geodetic reference): krasovsky
Latitude of 1st standard parallel (first standard weft): 25 degree
Latitude of 2nd standard parallel (second standard weft): 47 degree
Longitude of Central meridian (central meridian): 105 degree
Latitude of origin of prObjection (projection origin Latitude): 0 degree
False testing at central meridian (pseudo east): 0.00meters
False not rating at origin (pseudo north): 0.00meters
b) Parameter setting
Because the CCD linear arrays of the Beijing I minisatellite dual-multispectral imager are obliquely arranged at a certain included angle, the two CCD linear arrays share a projection center, and the included angle of the perpendicular line of the central points of the CCD linear arrays is 25.2896 degrees (as shown in figure 2). Aiming at the characteristic of oblique photography imaging of a Beijing I small satellite multispectral imager, images acquired by the imager 1 and the imager 2 are respectively subjected to orthorectification treatment by adopting a push-broom model. The parameters of the push-broom model are set as:
an imager 1:
focal length: 150 mm
Pixel size: 0.007 mm
Scan width: 9984 image element
Side view angle: 12.6448 degrees
The imager 2:
focal length: 150 mm
Pixel size: 0.007 mm
Scan width: 9984 image element
Side view angle: +12.6448 degree
c) Control point selection
The control point selection is completed under the condition of 2-3 times of image amplification. The control points should ensure that the panoramic image is controlled and evenly distributed. The point location is selected on the ground with little elevation change, generally on the intersection point of the linear ground features close to the orthogonal, the inflection point of the ground features, the obvious ground feature point with clear image or the fixed point ground features. Points are not allowed to be selected on high-rise buildings such as buildings, enclosing walls, water towers and the like. The control point maximum median error does not exceed the 2 times specified in table 1.
Table 1 error units in control points: pixel
| Terrain classification | Mean error |
| Plain and hilly land | 0.5～1.5 |
| Mountain land and high mountain land | 1.5～2 |
Control points are selected on the topographic map, and the number of the control points is determined according to the topographic conditions of the image. For flat land and hilly land, the number of control points of each scene (9984 pixels multiplied by 9984 pixels) image is not less than 30; in mountainous regions and high mountainous regions, the number of control points of each scene (9984 pixels multiplied by 5500 pixels) image is not less than 40. The number of control points is properly increased in the adjacent scene overlapping area, and is generally not less than 10 control points with the same name.
d) Digital elevation model selection
Elevation information was built using an ASTGTM 30 meter DEM as a push-broom model.
(3) Ortho image sampling output
The orthoimage is resampled by adopting a bicubic convolution sampling mode, and the sampling interval is 32 meters.
(4) Plane positioning precision evaluation of ortho image output result
And (4) evaluating the plane positioning accuracy of the output result of the orthoimage, and if the plane positioning accuracy does not reach the specification of drawing accuracy of 1: 250000 scale, selecting control points and outputting the image samples again.
The plane positioning precision evaluation adopts a check point selection method: the check point selecting method selects the homonymous points of the orthophoto map and the field check point or the topographic map, and calculates the error of the orthophoto map and the field check point or the topographic map. The number of the check points of each scene image is generally not less than 20 points, and the point positions are uniformly distributed. The accuracy evaluation formula is as follows:
RMS is the error in the point location, N is the number of checkpoints, uiIs x of the inspection point on the ortho-image,y coordinate, viIs the X, Y coordinates of a topographical map or field check point.
The errors in the point locations of the feature points of the orthophoto map relative to the feature points of the same name in the field are in accordance with the specifications of table 2.
Table 2 units of plane positioning accuracy of small satellite orthophoto maps: rice and its production process
| Terrain classification | Error in point location |
| Plain and hilly land | 125 |
| Mountain land and high mountain land | 187 |
In special areas (such as deserts, swamps and the like), errors in point positions are widened by 0.5 time, and the maximum error is not more than 2 times of the error.
Step four: automatic blocking and cataloguing of multispectral ortho-images of minisatellites
(1) National land area data partitioning strategy
On the basis of fully considering the geographical spatial characteristics, the data characteristics of small satellites, the software and hardware bearing capacity and the processing speed of China, the national land and soil area is divided into 10 multiplied by 8 sub-areas, the image size of each sub-area is 17125 (east-west direction) multiplied by 17700 (south-north direction) pixels, about 870MB, 1000 pixels are overlapped among the sub-areas, and 53 sub-areas actually covering the national land are provided (as shown in figure 4).
(2) Sub-region cataloging method
The nationwide land sub-regions are automatically cataloged according to the sequence from top to bottom and from left to right according to the 'C + column number (1-10) + R + row number (1-8)' (as shown in figure 5). Under the directory of each sub-region, all the orthoimage files falling into the range of the sub-region are saved, and the names of the image files are kept unchanged along with the original file names; meanwhile, in order to reserve the geographic coordinates of the sub-region image, a coordinate file (in the format of a × tfw file) with the same name as the directory is also included under the directory.
(3) Automatic blocking and cataloguing of multispectral ortho-images of minisatellites
The multispectral ortho-image of the small satellite is stored according to scenes, so that image data related to the production process of nationwide or regional mosaic products are more in source files and large in data volume. If each image file is processed by the traditional manual processing method according to the blocking strategy and the cataloguing method, the processing steps are more, the workload is huge, and the working efficiency is low. The invention develops a set of software for automatically picking up, partitioning and cataloging data in the region mosaic by utilizing the partitioning strategy and the cataloging method. The software can automatically complete the process only by inputting the information of the catalog of the ortho-image, the range of the mosaic area, the size of the subarea, the number of the overlapped pixels, the output catalog and the like at one time. Automatically cataloging all sub-areas in the area; automatically judging which image data files are contained in the sub-area, automatically carrying out batch processing on the image data files, reserving coordinate information of the image data files, and naming and outputting the image data files according to the original data file names. Manual intervention is not needed, and the working efficiency is greatly improved. Figure 5 illustrates a moonlet multispectral ortho image automatic blocking and cataloging process.
Step five: color uniformity and subregion tessellation
The sub-regions are used as processing units, color adjustment is carried out on each part of image used in the sub-regions by adopting methods such as linear stretching, nonlinear stretching and brightness contrast, the visual effect of the image is improved, the basic consistency of the color of each part of data is ensured, feathering is carried out on the data overlapping part of each part of data to ensure that the transition of the color of the data in the sub-regions is smooth and the color mutation phenomenon does not exist, and then all the image data in the sub-regions are subjected to mosaic processing to obtain sub-region image data with consistent color tone. The color adjustment method between sub-regions adopts a method of inter-sub-region joint tone (as shown in fig. 6), that is, taking image data of one sub-region as a tone reference, using an overlapping region between sub-regions to perform tone transfer, adjusting four adjacent images (marked as A, B, C, D), then adjusting the adjacent images respectively with A, B, C, D as a reference, and so on, and finally achieving uniform tone of the whole region.
The breadth of China is wide, the number of actually covered sub-areas is 53, and if a reference image is simply selected as a center, color deviation and distortion of some areas can be caused. To avoid this, a plurality of reference images may be selected nationwide. The present embodiment selects the C3R4, C8R4, C7R7 sub-regions as reference images.
Step six: simulated color conversion
And carrying out simulated color automatic conversion on the Beijing I false color data subjected to color homogenizing treatment to form 32 m simulated color image data of the Beijing I minisatellite. The color conversion formula is as follows:
red natural color 0.40 × false color near red band +0.60 × false color red band +0.10 × false color green band
Green natural color 0.65 near red band-0.12 near red band +0.60 near green band
Blue natural color 0.0 false color near red band +0.20 false color red band +0.90 false color green band
Step seven: seamless tessellation between sub-regions
Since the geographic coordinate information of the sub-region image files is reserved in the processing, all the sub-region image files can be directly subjected to seamless mosaic in common remote sensing processing software (software such as ERDAS, ENVI and the like).
Step eight: drawing
After seamless mosaic, the multispectral data volume of a 32-meter minisatellite in the country reaches more than 60 GB, so when a whole image or a regional image is manufactured, the multispectral 32-meter data of the minisatellite is firstly resampled according to a designed scale and drawing resolution, and the spatial resolution of a sampled image is shown in the following formula:
according to the national relevant standards such as remote sensing image plan making standard (GB 15968 + 1995), various national or regional scale image maps (such as fig. 7) are made in CorelDraw and Photoshop software according to the requirements of map finishing (namely adding necessary drawing elements such as frames, notes, ground object names, administrative boundaries, graticules, north pointers, mathematical bases (projection modes, parameters and scales), legends and the like). The remote sensing image is required to be basically consistent with vector data registration such as an administrative boundary, the error in position precision flat ground and hilly ground is not more than 0.50mm on the graph, and the error in mountain land and high mountain area is not more than 1.0 mm.
Claims (8)
1. A method for inlaying and manufacturing a small satellite region image map comprises the following steps:
the method comprises the following steps: obtaining microsatellite multispectral image data
Step two: microsatellite multispectral image data preprocessing
Preprocessing each orbit data to obtain a moonlet multispectral radiation correction image product;
step three: microsatellite multispectral image ortho-rectification
The method adopts a general push-broom model to carry out orthorectification processing on the multispectral image, and comprises the following implementation steps:
(1) collecting reference data
(2) Establishing an image orthorectification model
(3) Ortho image sampling output
(4) Evaluating the plane positioning precision of the output result of the orthoimage;
step four: automatic blocking and cataloguing of multispectral ortho-images of minisatellites
Dividing the mosaic area into a plurality of sub-areas based on the geographic space characteristics, the data characteristics of the microsatellite, the software and hardware bearing capacity and the processing speed of the mosaic area, and automatically cataloguing each sub-area; then, cutting the multispectral ortho-image of the small satellite according to the range of the sub-region, and respectively storing the cut image files into corresponding sub-region directories; meanwhile, each directory also comprises a coordinate file;
step five: color uniformity and subregion tessellation
Taking the subareas as processing units, and firstly, respectively carrying out uniform color processing and embedding on each subarea to obtain subarea image data with consistent color tones; then, adjusting the colors among the sub-regions by adopting a method of joint tone among the sub-regions to realize uniform tone of images of all the sub-regions;
step six: simulated color conversion
Carrying out simulated color conversion on the microsatellite false color data subjected to color homogenizing treatment to generate a simulated color product;
step seven: seamless tessellation between sub-regions
Seamlessly embedding all the sub-region files by using the geographic coordinate information of the sub-region files;
step eight: drawing
And (4) making various regional image maps with various scales according to the designed scales and the requirements of picture finishing according to related drawing standards.
2. The method of mosaicing and creating a microsatellite region image map of claim 1 wherein: the mosaic area is a Chinese land area, when the multispectral ortho-image of the small satellite is automatically blocked, the Chinese land territory area is divided into 10 multiplied by 8 sub-areas, each sub-area is 17125 multiplied by 17700 pixels from east to west, 1000 pixels are overlapped among the sub-areas, and 53 sub-areas actually covering the Chinese land are provided.
3. The method for mosaicing and creating a microsatellite region image map as recited in claim 1 or 2 wherein: when the multispectral ortho-image of the small satellite is automatically cataloged, the cataloging method comprises the following steps: and automatically cataloging the sub-regions according to the sequence from top to bottom and from left to right, wherein the sub-regions are subjected to the step of 'C + column number + R + row number'.
4. The method of mosaicing and creating a microsatellite region image map of claim 1 wherein: and step five, adjusting the color of the data overlapping part in the same sub-area by adopting a feathering processing method.
5. The method of mosaicing and creating a microsatellite region image map of claim 1 wherein: in the fifth step, the method of inter-sub-region joint tone uses a sub-region image as a tone reference, performs tone transmission by using the overlapping region between sub-regions, adjusts the four adjacent images, which are recorded as A, B, C, D, then adjusts the images adjacent to the sub-regions by using A, B, C, D as a reference, and so on, and finally achieves uniform tone of the whole region.
6. The method of mosaicing and creating a microsatellite region image map of claim 1 wherein: when the color conversion is simulated in the sixth step, the conversion formula is as follows:
red natural color 0.40 × false color near red band +0.60 × false color red band +0.10 × false color green band
Green natural color 0.65 near red band-0.12 near red band +0.60 near green band
Blue natural color is 0.0 × false color near red band +0.20 × false color red band +0.90 × false color green band.
7. The method of mosaicing and creating a microsatellite region image map of claim 1 wherein: when the control points in the third step are selected, the errors in the control points meet the following requirements: the error in the control point is within the range of 0.5, 1.5 for plain and hilly land, and within the range of 1.5, 2 for mountain and mountain land.
8. The method of mosaicing and creating a microsatellite region image map of claim 1 wherein: the detection result of the positioning precision of the middle plane in the step three meets the following requirements: for plain and hilly lands, the plane positioning precision is less than or equal to 125 meters; for mountain land and high mountain land, the plane positioning precision is less than or equal to 185 meters.
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| CN112419156A (en) * | 2020-11-26 | 2021-02-26 | 武汉大学 | Multi-high-resolution remote sensing image mosaic method considering ground object type difference |
| CN113888416A (en) * | 2021-09-10 | 2022-01-04 | 北京和德宇航技术有限公司 | Processing method of satellite remote sensing image data |
| CN113920030A (en) * | 2021-10-25 | 2022-01-11 | 自然资源部国土卫星遥感应用中心 | Large-area high-fidelity satellite remote sensing image uniform color mosaic processing method and device |
| CN113920030B (en) * | 2021-10-25 | 2022-04-08 | 自然资源部国土卫星遥感应用中心 | Large-area high-fidelity satellite remote sensing image uniform color mosaic processing method and device |
| CN115014294A (en) * | 2022-05-18 | 2022-09-06 | 河海大学 | River channel agile imaging method for east-west satellite |
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