CN106920235B - Automatic correction method for satellite-borne optical remote sensing image based on vector base map matching - Google Patents
Automatic correction method for satellite-borne optical remote sensing image based on vector base map matching Download PDFInfo
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Abstract
The invention provides a satellite-borne optical remote sensing image automatic correction method based on vector base map matching, which is characterized by comprising the following steps of: a, geocoding an image to be corrected according to a specified resolution; b, converting the vector diagram into a binary grid image according to the specified resolution, and unifying the resolution of the image to be corrected and the vector diagram; step C, taking the vector diagram as a control base diagram, and determining a common area between the images according to the geographic information of the image to be corrected and the control base diagram; and D, performing automatic matching processing on the image to be corrected and the binary control base map with the same resolution in the common area determined in the step C, and correcting the remote sensing image by taking the obtained matching point pairs as control points. The method has the characteristics of good stability and high positioning precision, expands the types of control base maps which can be used for automatic correction processing of the satellite-borne optical remote sensing image, and effectively solves the problem of automatic correction of the satellite-borne optical remote sensing image without the coverage area of the traditional optical control image.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a satellite-borne optical remote sensing image automatic correction method based on vector base map matching.
Background
With the successful emission of various optical remote sensing satellites (high-resolution one-number satellites, high-resolution two-number satellites, resource three-number satellites and the like), the data volume of remote sensing images is larger and larger, and in order to enable the remote sensing image data to really become a carrier of geospatial information and support subsequent application, each pixel of the remote sensing images needs to be endowed with accurate geographic position information, which puts higher and higher requirements on the automatic accurate correction technology of the remote sensing images. At present, in order to realize high-precision positioning of a satellite-borne optical remote sensing image, a method of controlled correction is needed, and the method usually adopts the optical image with high-precision positioning as a control base map and obtains control information of an image to be corrected through an automatic matching method. However, the coverage of the optical control image for high-precision positioning is limited, and the positioning accuracy of the existing data cannot be guaranteed.
At present, automatic geometric correction of satellite-borne remote sensing images is generally completed through automatic matching with an optical control base map, and the problems of poor precision and low efficiency of an artificial geometric correction method of the remote sensing images are solved (Zhang Dunkun, an automatic geometric fine correction algorithm [ J ] of satellite-borne remote sensing images based on image matching, a remote sensing technology and application, 2006,23(5),545 and 550).
However, the conventional automatic correction of the satellite-borne remote sensing image has the following disadvantages:
(1) the existing satellite-borne optical remote sensing image automatic correction processing is mostly realized based on an optical control base map, and the problem of limited control data coverage exists. The making of the optical control base map data is usually realized by determining ground control points through manual on-site surveying and mapping, a large amount of manpower and material resources are consumed, the data coverage range with higher confidence coefficient is limited, a large number of areas cannot provide optical control data, and automatic correction processing of satellite-borne remote sensing images cannot be performed.
(2) At the present stage, automatic correction of the satellite-borne optical remote sensing image is realized by automatic matching processing of homologous raster data, images to be matched are raster data, and texture information image pairs are abundant. However, the vector image only records the positioning information of the target of interest, and unlike the information recorded by the raster image, the existing matching algorithm cannot be applied to the automatic matching processing of the remote sensing image and the raster image, and an algorithm suitable for the automatic matching of the satellite-borne optical remote sensing image and the vector control base map needs to be designed.
(3) In the existing satellite-borne optical remote sensing image automatic correction method, a matching method based on feature descriptors and regional statistics correlation is often used, the intra-scene geometric distortion problem existing in the satellite-borne remote sensing image is not considered, and the positioning accuracy of the slice center is influenced.
In a word, the existing satellite-borne remote sensing image automatic correction processing method is limited by the coverage area of the optical control image, the data type which can be used as a control base map needs to be expanded, a corresponding automatic matching method is designed according to the characteristics of the satellite-borne optical remote sensing image and the vector base map, and the stability and the accuracy of the automatic correction process are guaranteed.
In summary, the existing satellite-borne optical remote sensing image automatic correction is mainly realized based on optical control base map automatic matching, and the research for realizing satellite-borne remote sensing image automatic correction based on vector base map matching is very few. With the increase of the remote sensing data volume, how to utilize different control base map information is a problem to be solved by expanding the coverage range of the satellite-borne remote sensing image capable of being automatically corrected.
Disclosure of Invention
Technical problem to be solved
In view of the technical problems, the invention provides a method for automatically correcting an spaceborne optical remote sensing image based on vector base map matching, which has the characteristics of good stability and high positioning precision, expands the types of control base maps which can be used for automatically correcting the spaceborne optical remote sensing image, and effectively solves the problem of automatically correcting the spaceborne optical remote sensing image without the coverage area of the traditional optical control image.
(II) technical scheme
According to one aspect of the invention, the invention provides a satellite-borne optical remote sensing image automatic correction method based on vector base map matching, which comprises the following steps: a, geocoding an image to be corrected according to a specified resolution; b, converting the vector diagram into a binary grid image according to the specified resolution, and unifying the resolution of the image to be corrected and the vector diagram; step C, taking the vector diagram as a control base diagram, and determining a common area between the images according to the geographic information of the image to be corrected and the control base diagram; and D, performing automatic matching processing on the image to be corrected and the binary control base map with the same resolution in the common area determined in the step C, and correcting the remote sensing image by taking the obtained matching point pairs as control points.
Preferably, in the step a, the image to be corrected is geocoded by using an RPC file, and a relation between an image plane coordinate (Line, Sample) and a ground three-dimensional coordinate (Longitude, Latitude, Height) of the original image to be corrected is determined by using a rational polynomial model.
Preferably, in the step B, the pixel value of the vector line segment passing position in the vector image is set to 255, and the pixel of the vector line segment non-passing position is set to 0, so as to obtain a binary grid image with the same resolution as the image to be matched.
Preferably, the step D includes: a substep D1, selecting the binaryzation grid image slices with rich texture as control point slices, and ensuring the slices to be uniformly distributed; a substep D2, processing the image to be corrected by using a LoG detection algorithm, and extracting the feature of the ground object target; a substep D3, carrying out weighted template matching on the vector control point slice and the image slice after the ground object target characteristics are extracted; and a substep D4, screening matching point pairs by using a Randac method, removing the matching points with larger errors, and taking the obtained matching point pairs as control points to correct the remote sensing image.
Preferably, in the sub-step D1, the binarized control base map is divided into a series of regions according to the size W × W, and a slice with the size W × W and the most abundant texture is selected as the control point slice in each region.
Preferably, the degree of information richness of a slice is measured by counting the number of pixels with a grayscale value greater than 0 within the slice.
Preferably, in the sub-step D2, the image to be corrected is processed by using a LoG detection algorithm, and the feature of the center line of the ground object target is extracted as the feature of the ground object target.
Preferably, the LoG detection algorithm includes: gaussian filtering is used for smoothing image noise, a Laplacian operator calculates second-order derivative enhanced linear structure information, and the linear structure positioning precision is improved through second-order derivative zero-crossing point detection and linear interpolation.
Preferably, in the LoG detection algorithm, a LoG detection operator scale is selected according to the size of the actual ground object target and the image resolution information.
Preferably, the weighted template matching formula is as follows:
wherein, Ccoef(u, v) represents a similarity coefficient of template matching, u, v represent coordinates of a sliding window, x, y represent coordinates of a template matching center, I and T represent a reference image and a template image, respectively, w (u, v) is a weight coefficient in inverse relation to a distance of the matching center (x, y):
wherein e is a weight coefficient adjustment constant, mu1And mu2Weighted gray-scale mean for images I and T:
where N represents the number of pixels contained in the template image.
(III) advantageous effects
According to the technical scheme, the automatic correction method of the satellite-borne optical remote sensing image based on the vector base map matching has at least one of the following beneficial effects:
(1) the vector base map is used as a control map, the control base map types which can be used for automatic correction processing of the satellite-borne optical remote sensing image are expanded through geographic coding with specified resolution, the scale and rotation difference between images to be matched is avoided, and the problem of automatic correction of the satellite-borne optical remote sensing image in the area without coverage of the traditional optical control image is effectively solved.
(2) Through counting the number of nonzero pixels in the binary grid image, the section with rich texture is quickly selected in the blocking area as the section to be matched, so that the stability of the matching process is improved, and the distribution uniformity of the control points is obtained.
(3) The size of the LoG operator is determined by combining the size of the ground object target and the image resolution, and the method is different from the target edge extracted by a common edge detection algorithm.
(4) The control point positioning precision deviation caused by the in-scene distortion of the satellite-borne optical remote sensing image is corrected by a weighted template matching method, the influence of the in-scene distortion on a matching result is removed, the accuracy of the correction result is ensured, the automatic correction precision of the image is improved, and the stability is good.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings. Like reference numerals refer to like parts throughout the drawings. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention.
Fig. 1 is a flowchart of an automatic correction method for satellite-borne optical remote sensing images based on vector base map matching according to an embodiment of the present invention.
Fig. 2 is a vector slice diagram of an automatic correction method for satellite-borne optical remote sensing images based on vector base map matching according to an embodiment of the present invention.
Fig. 3 is a diagram of an automatic matching result between a satellite-borne optical remote sensing image and a vector slice of the automatic correction method for the satellite-borne optical remote sensing image based on vector base map matching in the embodiment of the present invention.
Fig. 4 is a distribution diagram of a calibration precision check point of a satellite-borne optical remote sensing image automatic calibration method based on vector base map matching in the embodiment of the present invention.
Fig. 5 is a comparison inspection result diagram of positioning accuracy before and after processing a high-resolution first-order remote sensing image by the automatic correction method for the satellite-borne optical remote sensing image based on vector base map matching in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It should be noted that in the drawings or description, the same drawing reference numerals are used for similar or identical parts. Implementations not depicted or described in the drawings are of a form known to those of ordinary skill in the art. Additionally, while exemplifications of parameters including particular values may be provided herein, it is to be understood that the parameters need not be exactly equal to the respective values, but may be approximated to the respective values within acceptable error margins or design constraints. Directional phrases used in the embodiments, such as "upper," "lower," "front," "rear," "left," "right," and the like, refer only to the orientation of the figure. Accordingly, the directional terminology used is intended to be in the nature of words of description rather than of limitation.
The invention provides a vector base map matching-based automatic correction method for satellite-borne optical remote sensing images, which has the characteristics of good stability and high positioning precision, expands the types of control base maps which can be used for automatic correction processing of satellite-borne optical remote sensing images, and effectively solves the problem of automatic correction of satellite-borne optical remote sensing images without coverage areas of traditional optical control images.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The invention provides a satellite-borne optical remote sensing image automatic correction method based on vector base map matching. Fig. 1 is a flowchart of an automatic correction method for satellite-borne optical remote sensing images based on vector base map matching according to an embodiment of the present invention. Referring to fig. 1, the method for automatically correcting a satellite-borne optical remote sensing image based on vector base map matching according to the embodiment of the present invention includes:
a, geocoding an image to be corrected according to a specified resolution;
b, converting the vector diagram into a binary grid image according to the specified resolution, and unifying the resolution of the image to be corrected and the vector diagram;
step C, taking the vector diagram as a control base diagram, and determining a common area between the images according to the geographic information of the image to be corrected and the control base diagram;
and D, performing automatic matching processing on the image to be corrected and the binary control base map with the same resolution in the common area determined in the step C, and correcting the remote sensing image by taking the obtained matching point pairs as control points.
Specifically, in the step a, the image to be corrected is geocoded by using an RPC file, and a rational polynomial model is used to determine a relationship between an image plane coordinate (Line, Sample) and a ground three-dimensional coordinate (Longitude, Latitude, Height) of the original image to be corrected:
wherein, an,bn,cn,dnThe RPC parameters (where n is 1, 2, 3 … … 20), P, L, H are normalized ground coordinates, and X, Y are normalized image plane coordinates, with the relationship between them as shown below:
wherein LAT _ OFF, LAT _ SCALE, LONG _ OFF, LONG _ SCALE, HEIGHT _ OFF, HEIGHT _ SCALE are regularization parameters of ground coordinates; SAMPLE _ OFF, SAMPLE _ SCALE, LINE _ OFF and LINE _ SCALE are regularization parameters of image coordinates; longitude, Latitude and Height denote ground three-dimensional coordinates (Longitude, Latitude and elevation); line and Sample represent image plane coordinates (lines, columns) of an original image.
In the step B, the vector image is converted into a binarized raster image according to a specified resolution, and according to the specified resolution, the pixel value of the passing position of the vector line segment is 255, and the pixel value of the passing position of the vector line segment is 0, so that a binarized raster image with the same resolution as the image to be matched can be obtained.
In the step D, performing automatic matching processing on the same-resolution satellite-borne optical remote sensing image and the binary vector base map, including:
and a substep D1, selecting the binary grid image slices with rich texture as control point slices and ensuring the slices to be uniformly distributed. Firstly, dividing a binary vector base map into a series of regions according to the size of W x W, and selecting a slice with the size of W x W and the most abundant texture as a control point slice in each region, so that the stability of a subsequent matching process is improved, and the distribution uniformity of the selected slices is also ensured. Since the vector image has been converted into a binarized raster image, the degree of information richness of the slice can be measured by counting the number of pixels having a grayscale value greater than 0 within the slice.
And a substep D2 of processing the image to be corrected by using a LoG (Laplacian of the Gaussian function) detection algorithm and extracting the feature of the ground object target. Preferably, the center line feature of the ground object target is extracted as the feature of the ground object target, so that the ground object feature represented by the vector diagram can be well corresponded.
The LoG detection algorithm comprises the following steps: gaussian filtering and smoothing image noise, Laplacian operator calculating second-order lead enhancement linear structure information, linear structure detection through second-order lead zero crossing points and linear interpolation are adopted to improve the positioning accuracy of the linear structure. Since the vector base map (such as a road and river vector map) marks the position of the target centerline, the remote sensing image can be processed by using a log (laplacian of the Gaussian function) detection operator to extract the feature of the target centerline of the ground object. Compared with the road edge line characteristics extracted by the Sobel operator, the Canny operator and the like, the LoG operator used by the method can effectively improve the positioning accuracy of the matching result. The selection of the LoG detection operator dimension needs to consider the size of the actual ground object target and the image resolution information. Taking a road vector diagram as an example, the vector-labeled ground object target is an urban road, the road width is about ten meters, the resolution of the first-grade high-resolution image is 2 meters, the road width is 5 pixels, and the size of the filtering template is 5 pixels.
And a substep D3 of performing weighted template matching on the vector control point slice and the image slice after the linear feature extraction.
Due to the influence of factors such as topographic relief, camera distortion and the like, a certain in-scene distortion problem exists in the satellite-borne optical remote sensing image, and the coordinate of the matching center point is used as the coordinate of the control point in the automatic correction process, so that the area information near the center point should occupy a larger weight in the matching process, and the accuracy of the matching result can be effectively improved through the weighted template matching method.
The weighted template matching formula is as follows:
wherein, Ccoef(u, v) represents a similarity coefficient of template matching, u, v represents coordinates of a sliding window, and x, y represents a template matching center coordinateThe standard, I and T represent the reference image and template image, respectively, w (u, v) is a weight coefficient in inverse relation to the distance of the matching center (x, y):
wherein e is a weight coefficient adjustment constant, mu1And mu2Weighted gray-scale mean for images I and T:
where N represents the number of pixels contained in the template image.
And a substep D4, screening matching point pairs by using a Randac method, removing the matching points with larger errors, and taking the obtained matching point pairs as control points to correct the remote sensing image.
The automatic correction method of the satellite-borne optical remote sensing image based on the vector base map matching is described in detail below by combining an example.
The method comprises the steps of firstly selecting an L1-grade satellite-borne optical image product in Beijing area one high mark, taking a public road vector diagram in Beijing city of 2013 as a control base diagram, then adopting the method provided by the invention to carry out automatic correction processing, uniformly converting a raster image and a vector base diagram into a geocoded image with the resolution of 2 meters, dividing a common area of an image to be corrected and the vector base diagram into a series of uniformly distributed areas according to the size of 8000m × 8000m, and selecting a slice with the size of 2000m × 2000m and the most abundant texture in each area, wherein (a) and (b) in FIG. 2 respectively show two vector control point slices with different texture richness degrees, compared with the (a) in FIG. 2, the information amount which can be used for automatic matching processing in the (b) in FIG. 2 is large, and the stability of automatic matching can be improved by adopting the (b) in FIG. 2 as a control slice to carry out automatic matching.
The initial positioning deviation of the top-score first image is within 50 meters, the template matching search range is set to be 50/2-25 pixels, and the result of automatic matching of the top-score first image and the vector slice is shown in fig. 3.
The number of slice data obtained by automatic matching is 46, automatic correction of the satellite-borne optical remote sensing image is carried out based on control points, and the processing time is 38s by adopting an Intel i7-4790 CPU. The images before and after being corrected by the method of the invention are checked for positioning accuracy, 10 uniformly distributed check points are used in total, and the distribution condition of the check points in the image range is shown in figure 4.
The vector base map is taken as a reference, the high-resolution first-order remote sensing images are respectively subjected to uncontrolled correction and correction processing of the satellite-borne optical remote sensing image automatic correction method based on the vector base map matching, and partial positioning accuracy inspection results of the images obtained by two different processing methods are shown in fig. 5. According to the checking results of the positioning accuracy before and after correction, the uncontrolled geometric positioning accuracy of the high-resolution first-order image is 21.3 meters based on the vector base map, and the geometric positioning accuracy of the image after correction processing by adopting the method is 1.3 meters.
It should be understood by those skilled in the art that the above implementation methods are only used for illustrating the present invention and are not to be taken as limitations of the present invention, and that the changes and modifications of the above examples are within the scope of the present invention
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. Furthermore, the above definitions of the various elements and methods are not limited to the particular structures, shapes or arrangements of parts mentioned in the examples, which may be easily modified or substituted by one of ordinary skill in the art, for example:
(1) the LoG detection operator is used for extracting the grid image target central linear feature, edge detection algorithms such as a Sobel operator, a prewitt operator, a Roberts operator and a Canny operator can also be used, and the method belongs to the scope of the invention as long as the existing edge detection operator is used for grid image linear feature detection.
(2) The control base map of the invention can be a road vector map, and can also adopt vector base maps of rivers, coastlines and the like, without influencing the realization of the invention.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A satellite-borne optical remote sensing image automatic correction method based on vector base map matching is characterized by comprising the following steps:
a, geocoding an image to be corrected according to a specified resolution;
b, converting the vector diagram into a binary grid image according to the specified resolution, and unifying the resolution of the image to be corrected and the vector diagram;
step C, taking the vector diagram as a control base diagram, and determining a common area between the images according to the geographic information of the image to be corrected and the control base diagram;
d, performing automatic matching processing on the image to be corrected and the binary control base map with the same resolution in the common area determined in the step C, and correcting the remote sensing image by taking the obtained matching point pairs as control points;
in the step B, setting a pixel value of a passing position of a vector line segment in the vector diagram to be 255, and setting a pixel of a passing position of no vector line segment to be 0, so as to obtain a binary grid diagram with the same resolution as the image to be matched;
in the step A, the image to be corrected is geocoded through an RPC file, and the relationship between the image plane coordinates (Line, Sample) and the ground three-dimensional coordinates (Longitude, Latitude, Height) of the original image to be corrected is determined by a rational polynomial model:
wherein, an，bn，cn，dnThe method comprises the following steps of (1) obtaining RPC parameters, wherein n is 1, 2 and 3.. 20, P, L and H are normalized ground coordinates, and X and Y are normalized image plane coordinates; LAT _ OFF, LAT _ SCALE, LONG _ OFF, LONG _ SCALE, HEIGHT _ OFF, HEIGHT _ SCALE are regularization parameters for ground coordinates; SAMPLE _ OFF, SAMPLE _ SCALE, LINE _ OFF and LINE _ SCALE are regularization parameters of image coordinates; longitude, Latitude and Height respectively represent Longitude, Latitude and elevation of ground three-dimensional coordinates; line and Sample respectively represent image plane coordinate lines and lines of the original image;
the step D comprises the following steps:
a substep D1, selecting the binaryzation grid image slices with rich texture as control point slices, and ensuring the slices to be uniformly distributed;
a substep D2, processing the image to be corrected by using a LoG detection algorithm, and extracting the feature of the ground object target;
a substep D3, carrying out weighted template matching on the vector control point slice and the image slice after the ground object target characteristics are extracted;
substep D4, screening matching point pairs by using a Randac method, removing the matching points with larger errors, and taking the obtained matching point pairs as control points to correct the remote sensing image;
the weighted template matching formula is as follows:
wherein, Ccoef(u, v) represents a similarity coefficient of template matching, u, v represent coordinates of a sliding window, x, y represent coordinates of a template matching center, I and T represent a reference image and a template image, respectively, w (u, v) is a weight coefficient in inverse relation to a distance of the matching center (x, y):
wherein e is a weight coefficient adjustment constant, mu1And mu2Weighted gray-scale mean for images I and T:
where N represents the number of pixels contained in the template image.
2. The method for automatically correcting the satellite-borne optical remote sensing image based on the vector base map matching as claimed in claim 1, wherein in the substep D1, the binary control base map is divided into a series of regions according to the size of W x W, and a slice with the size of W x W and the most abundant texture is selected as the control point slice in each region.
3. The method for automatically correcting the satellite-borne optical remote sensing image based on the vector base map matching as claimed in claim 2, wherein the degree of information richness of the slice is measured by counting the number of pixels with the gray value larger than 0 in the slice.
4. The method for automatically correcting the satellite-borne optical remote sensing image based on the vector base map matching as claimed in claim 1, wherein in the substep D2, the image to be corrected is processed by using a LoG detection algorithm, and the centerline feature of the ground object target is extracted as the feature of the ground object target.
5. The method for automatically correcting the satellite-borne optical remote sensing image based on the vector base map matching as claimed in claim 1, wherein the LoG detection algorithm comprises: gaussian filtering is used for smoothing image noise, a Laplacian operator calculates second-order derivative enhanced linear structure information, and the linear structure positioning precision is improved through second-order derivative zero-crossing point detection and linear interpolation.
6. The automatic correction method of the satellite-borne optical remote sensing image based on the vector base map matching as claimed in claim 5, characterized in that in the LoG detection algorithm, LoG detection operator scale is selected according to the size of the actual ground object target and the image resolution information.
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| CN107341781B (en) * | 2017-07-20 | 2020-06-09 | 中国科学院电子学研究所 | SAR image correction method based on improved phase consistency feature vector base map matching |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126812A (en) * | 2007-09-27 | 2008-02-20 | 武汉大学 | High resolution ratio remote-sensing image division and classification and variety detection integration method |
| CN102800052A (en) * | 2012-06-13 | 2012-11-28 | 浙江大学 | Semi-automatic digital method of non-standard map |
| CN103218783A (en) * | 2013-04-17 | 2013-07-24 | 国家测绘地理信息局卫星测绘应用中心 | Fast geometric correction method for satellite remote sensing image and based on control point image database |
| CN103646389A (en) * | 2013-03-26 | 2014-03-19 | 中国科学院电子学研究所 | SAR slant range image match automatic extraction method based on geometric model |
| CN104778679A (en) * | 2014-12-22 | 2015-07-15 | 中国科学院遥感与数字地球研究所 | Gaofen-1 satellite data-based control point graphic element rapid-matching method |
| CN105631818A (en) * | 2015-12-24 | 2016-06-01 | 中国农业大学 | Batch automatic geometric correction method and device of remote sensing image |
-
2017
- 2017-02-28 CN CN201710109715.4A patent/CN106920235B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126812A (en) * | 2007-09-27 | 2008-02-20 | 武汉大学 | High resolution ratio remote-sensing image division and classification and variety detection integration method |
| CN102800052A (en) * | 2012-06-13 | 2012-11-28 | 浙江大学 | Semi-automatic digital method of non-standard map |
| CN103646389A (en) * | 2013-03-26 | 2014-03-19 | 中国科学院电子学研究所 | SAR slant range image match automatic extraction method based on geometric model |
| CN103218783A (en) * | 2013-04-17 | 2013-07-24 | 国家测绘地理信息局卫星测绘应用中心 | Fast geometric correction method for satellite remote sensing image and based on control point image database |
| CN104778679A (en) * | 2014-12-22 | 2015-07-15 | 中国科学院遥感与数字地球研究所 | Gaofen-1 satellite data-based control point graphic element rapid-matching method |
| CN105631818A (en) * | 2015-12-24 | 2016-06-01 | 中国农业大学 | Batch automatic geometric correction method and device of remote sensing image |
Non-Patent Citations (2)
| Title |
|---|
| A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery;Tengfei Long等;《remote sensing》;20160119;第8卷(第56期);第1-23页 * |
| 基于级联变换的光学和SAR图像配准算法;王峰等;《红外与毫米波学报》;20150815;第34卷(第4期);第1.2节、第1.3节 * |
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