CN113643240B - Rapid detection and correction method for local distortion of remote sensing image along track direction - Google Patents
Rapid detection and correction method for local distortion of remote sensing image along track direction Download PDFInfo
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Abstract
The application discloses a method for rapidly detecting and correcting local distortion of a remote sensing image along a track direction, which comprises the following steps: acquiring an imaging time data file and a meta information file of an image to be detected, and extracting information of the image to be detected from the meta information file, wherein the information of the image to be detected comprises a scene number, an image start absolute line count and an image end absolute line count of the image to be detected; acquiring integration time recorded by each line of an image start absolute line count-image end absolute line count part from an imaging time data file according to a scene number, and judging whether jump occurs in the integration time corresponding to each line by line; if jump occurs, generating early warning information, outputting information of the image to be detected, and line counting and integration time of the jump, wherein the early warning information is used for indicating that the image to be detected is locally distorted along the track direction. The application solves the technical problems of low detection efficiency and low accuracy in the prior art.
Description
Technical Field
The application relates to the technical field of remote sensing image detection, in particular to a rapid detection and correction method for local distortion of a remote sensing image along a track direction.
Background
A focal plane splicing camera of a first-class satellite charge coupled device (Charge Coupled Device, CCD) adopts 4 full-color multispectral five-color TDICCD to carry out reflector splicing to form a straight line. According to the accurate real position of each probe element in each spectral section of each CCD device in the camera, imaging time data, integration time data, attitude data, orbit data and scenery inventory information data of each line of image, sensor correction is carried out, an observation vector of each probe element is constructed, the problems of geometric distortion, geometric splicing, imaging time normalization, wave band registration and the like are uniformly solved, the uniformity of the resolution of each probe element is realized, and therefore the internal geometric precision of satellite images is ensured. If any of the above factors has a large error or mistake, the geometric model of the remote sensing image is wrong, and the geometric accuracy of the image is further deteriorated.
In the in-orbit operation of a first-order satellite in the high scene, the imaging time data jump situation (the occurrence probability is less than 1 per mill) happens, and the imaging data in a plurality of time periods before and after the jump time is corrected by a sensor can generate local image distortion along the in-orbit direction. Because of the time-sharing imaging of each wave band, the time of each wave band along the distortion of the track direction is the same, but the geographic positions are different, after the wave bands are synthesized, the wave bands with sub-pixels to a plurality of pixel levels are staggered in tens to hundreds of scanning lines, and the rest scanning lines are normal.
The existing automatic detection process for the distortion of the remote sensing image along the track direction mainly adopts SIFT algorithm, mutual information, phase consistency and other algorithms to acquire homonymous characteristic points to match the image, but the algorithms are high in calculation complexity and high in calculation amount. Because of the high scene, the number of images to be ordered and produced is large on average every day for four stars. If the existing algorithm is adopted to carry out computer automatic partial distortion detection along the track direction on all image data produced at the present time, a large amount of software and hardware resources are consumed, the time duration is long, the timeliness of data delivery cannot be ensured, and further the detection efficiency and timeliness are poor; in addition, since the number of lines in which distortion occurs in the track direction in one scene data is tens to hundreds, other scan lines are normal, if the obtained homonymous feature points are not in the range of the number of lines in which distortion occurs in the track direction, the problem of local distortion in the track direction, namely, missed detection, is very likely to be solved. The image with local distortion along the track direction has different geographic positions and distortion degrees, so that the extraction of the homonymous characteristic points in the area of the image with the distortion along the track direction is very easy to make mistakes or the homonymous points cannot be obtained, namely the error detection or the omission detection occurs. In addition, the following cases exist in the image data of the linear array push-broom imaging: the camera has different numbers of bad pixels at different positions in each wave band, and the bad pixels are represented as bad lines on the original image, and interpolation processing is needed. If 2 or more adjacent pixels are bad pixels, the texture or edge diffusion of the ground object can occur after interpolation processing. In particular, the broken wire falls on the edge of the ground object with larger difference of reflection characteristics (such as the edge of grassland and road, the edge of woodland and building, etc.), and the diffused edge wave bands are synthesized to be in a color ribbon shape, so that the band dislocation is easy to be detected by mistake. The gray resampling is carried out for a plurality of times in the geometric processing process of the image radiation, so that textures or edges (such as larger included angles with the rail along the rail and the vertical rail) at certain angles are in a zigzag shape; the texture of the water body and the desert image is single, the texture of the farmland and the woodland image is finer and finer, the control point automatic matching precision of the existing automatic detection can be affected, the false detection is caused, and the detection accuracy is poor.
Disclosure of Invention
The technical problem that this application solved is: in the scheme provided by the embodiment of the application, on one hand, the problems of high detection efficiency and low precision caused by manual detection are avoided through an automatic detection mode, and on the other hand, the problem of wrong detection caused by influence on automatic matching precision due to the fact that a plurality of gray resampling times performed in the geometric processing process of bad pixels and image radiation or water and desert image textures are single, farmland and woodland image textures are finer and broken and the like are caused in each band of a camera in the detection process of automatically matching images by acquiring homonymous feature points through SIFT algorithm, mutual information or phase consistency and other algorithm is avoided.
In a first aspect, an embodiment of the present application provides a method for rapidly detecting and correcting local distortion of a remote sensing image along a track direction, where the method includes:
acquiring an imaging time data file and a meta information file of an image to be detected, and extracting information of the image to be detected from the meta information file, wherein the information of the image to be detected comprises a scene number, an image start absolute line count and an image end absolute line count of the image to be detected;
acquiring integration time recorded by each line of an image starting absolute line count-image ending absolute line count part from an imaging time data file according to the scene number, and judging whether jump occurs in the integration time corresponding to each line by line;
if jump occurs, generating early warning information, outputting the information of the image to be detected and the line count and the integration time of the jump, wherein the early warning information is used for indicating that the image to be detected is locally distorted along the track direction.
Optionally, determining whether the integration time corresponding to each line in the content jumps line by line includes:
judging whether the integral time corresponding to each row in the content is less than 0 row by row; or (b)
And calculating first average integration time according to the integration time corresponding to each line of the image starting absolute line counting part to the image ending absolute line counting part, and judging whether the integration time corresponding to each line is larger than the first average integration time of a designated multiple line by line.
Optionally, the method further comprises: performing manual interpretation according to the early warning information indication to obtain a manual interpretation result; and generating a local distortion detection report along the track direction corresponding to the image to be detected according to the manual interpretation result and the early warning information.
Optionally, if the manual judging result is that the image to be detected has local distortion along the track direction, the method further includes:
calculating second average integration time according to the integration time recorded by each line in the imaging time data file, and determining first line count, first integration time and first line time corresponding to each line of at least one line with integration time jump in the imaging time data file;
smoothing the first line between two adjacent integration time jump lines according to the first line, the first integration time and the second average integration time corresponding to the two adjacent integration time jump lines to obtain corrected line;
and correcting the integration time corresponding to each line of the line with the integration time jump in the imaging time data file according to the corrected line to obtain corrected integration time.
Optionally, if the integration time hopping lines appear in the imaging time data file for the first time and the second time in the adjacent two times, smoothing the first line between the integration time hopping lines appearing in the adjacent two times according to the first line, the first integration time and the second average integration time corresponding to the integration time hopping lines appearing in the adjacent two times to obtain a corrected line, including:
and smoothing the first row between the two adjacent integration time jump rows to obtain a corrected row by the following steps:
LT re (i)＝LT(i)-IT(L 1 )+IT mean
wherein LT re (i) Representing the row corresponding to the i-th row after correction; LT (i) represents a row corresponding to the i-th row before correction; IT (L) 1 ) Represents the L < th 1 Integration time corresponding to the row; IT (information technology) mean Representing a second average integration time; l (L) 1 +1≤i≤L 2 ，L 1 Representing the first occurrence of an integration time jumpA first row count corresponding to the changed row L 2 Representing the first row count corresponding to the second occurrence of the integration time hopping row.
Optionally, if the integration time hopping behaviors occur in two adjacent times, where j is a positive integer not less than 2 and j+1th integration time hopping rows occur in the imaging time data file, smoothing the first row between the integration time hopping rows occur in two adjacent times according to the first row, the first integration time and the second average integration time corresponding to the integration time hopping rows occurring in two adjacent times to obtain a corrected row, including:
and smoothing the first row between the two adjacent integration time jump rows to obtain a corrected row by the following steps:
LT re (i)＝LT(i)-(LT(L j +1)-LT re (L j ))+IT mean
wherein L is j A first row count corresponding to a row representing a j-th occurrence of an integration time jump; LT (L) j +1) represents the row corresponding to the next row in which the integration time jump occurs the j-th time; LT (LT) re (L j ) Representing a corrected row corresponding to a row in which the integration time jump occurs the j-th time; j represents the j-th occurrence of integral time jump, wherein n is less than or equal to 2; l (L) j +1≤i≤L j+1 。
Optionally, correcting the integration time corresponding to each line of the imaging time data file in which the integration time jump line appears according to the corrected line to obtain corrected integration time, including:
correcting the integration time corresponding to each row by the following formula to obtain corrected integration time:
IT re (i)＝LT re (i+1)-LT re (i)
wherein IT is re (i) Indicating the integration time after the correction of the ith row; LT (LT) re (i+1) represents a corrected row of the i+1th row; LT (LT) re (i) Representing the row after the i-th row is corrected; i=l 1， L 2 ，，L n ，L 1， L 2 ，， n Representing the first row count.
Optionally, the method further comprises: and obtaining a new imaging time data file according to the corrected row and the corrected integration time, and regenerating a new remote sensing image according to the new imaging time data file.
Optionally, the method further comprises: performing geometric positioning accuracy detection on the new remote sensing image; and if the geometric positioning accuracy exceeds the limit, performing RPC refinement on the new remote sensing image according to a preset reference image to obtain an image meeting the requirement of the preset geometric positioning accuracy.
Compared with the prior art, the scheme provided by the embodiment of the application has at least the following beneficial effects:
1. in the scheme provided by the embodiment of the application, on one hand, the problems of high detection efficiency and low accuracy caused by manual detection are avoided through an automatic detection mode, and on the other hand, the problems of automatic matching precision and wrong detection caused by the fact that in the detection process of automatically matching images by acquiring homonymous characteristic points through SIFT algorithm, mutual information or phase consistency and other algorithms, a plurality of gray resampling or single water body and desert image textures performed in the geometric processing process of bad pixels and image radiation possibly exist in each wave band of a camera are avoided.
2. According to the scheme provided by the embodiment of the application, the scene number, the image start absolute line count and the image end absolute line count of the image to be detected are extracted from the meta-information file, the integration time recorded by each line of the image start absolute line count to the image end absolute line count part is obtained from the imaging time data file according to the scene number, whether the integration time corresponding to each line is jumped or not is judged line by line, the fact that the image possibly has local distortion along the track direction is determined according to the jump of the integration time, and the problems that the texture of a water body and a desert image is single, the texture of a farmland and a woodland image is finer and broken, the accuracy of automatic matching is affected, the detection is easy to miss, and the detection accuracy is low are avoided.
Drawings
Fig. 1 is a flow chart of a method for rapidly detecting and correcting local distortion of a remote sensing image along a track direction according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a meta information file according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an imaging time data file (. IT file) according to an embodiment of the present application;
FIG. 4 is a graph showing the variation of the integration time with the line count according to the embodiment of the present application;
FIG. 5a is a schematic diagram of an image before correction according to an embodiment of the present disclosure;
FIG. 5b is a schematic diagram of a corrected image according to an embodiment of the present disclosure;
FIG. 6a is a schematic diagram of still another pre-correction image according to an embodiment of the present disclosure;
fig. 6b is a schematic diagram of another corrected image according to an embodiment of the present application.
Detailed Description
In the solutions provided by the embodiments of the present application, the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following is a description of a method for rapidly detecting and correcting local distortion of a remote sensing image along a track direction provided in the embodiment of the present application in detail with reference to the accompanying drawings of the specification, and a specific implementation manner of the method may include the following steps (the method flow is shown in fig. 1):
step 101, acquiring an imaging time data file and a meta information file of an image to be detected, and extracting information of the image to be detected from the meta information file, wherein the information of the image to be detected comprises a scene number, an image start absolute line count and an image end absolute line count of the image to be detected.
Specifically, in the scheme provided by the embodiment of the application, when the satellite generates the level 0 data corresponding to the remote sensing image, the satellite generates and stores the corresponding auxiliary file at the same time, wherein the auxiliary file comprises a time data file (. IT file) and a meta information file (. XML file) and the like. The initial absolute line count and the end absolute line count corresponding to the multispectral and full-color images are recorded in a meta-information file (. XML file), wherein the absolute line count refers to the line count value of the data corresponding to the images to be detected in the data corresponding to the whole stripe. The application is mainly aimed at detecting distortion along the track direction between multispectral wave bands, so that only the initial absolute line count and the end absolute line count of data corresponding to multispectral images are required to be extracted from a meta-information file.
In order to facilitate understanding the above-mentioned process of extracting the start absolute line count and the end absolute line count of the data corresponding to the image to be detected from the meta-information file, a brief description will be given below.
Referring to fig. 2, a schematic structure of a meta information file (.xml file) is provided in an embodiment of the present application. In fig. 2, the scene number, satellite identification, receiving track number, imaging track number, stripe number, imaging mode, imaging data start absolute line count (L1), end absolute line count (L2) and the like corresponding to the image to be detected are respectively read, that is, the scene number (2904594) corresponding to the < SceneID > field, the satellite identification (GJ 1B) corresponding to the < SatelliteID > field, the receiving track number (GUA) corresponding to the < Receive Station ID > field, the imaging track number (18501) corresponding to the < OrbitID > field, the stripe number (18493) corresponding to the < polbitid > field, the imaging mode (168597) corresponding to the < DatasetID > field, the end absolute line count (699138, 27962) corresponding to the < SceneStartLin > field, and the start absolute line count (692339,2770471) corresponding to the < SceneStopLine > field are respectively read.
Step 102, acquiring the integration time recorded by each line of the image start absolute line count to the image end absolute line count part from the imaging time data file according to the scene number, and judging whether jump occurs in the integration time corresponding to each line by line.
Specifically, the computer equipment acquires the scene number of the image to be detected and the imaging start absolute line countAfter finishing the absolute line count, opening an imaging time data file (IT file) corresponding to the strip where the image to be detected is located according to the scene number, and then intercepting the initial absolute line count (L) of the image to be detected from the IT file star ) To end absolute line count (L stop ) Content between them, then judging L line by line star To L stop Whether the integration time corresponding to each row jumps or not. In the solution provided in the embodiment of the present application, there are various ways of determining whether the integration time corresponding to each row is hopped, and one of them is taken as an example for illustration.
In one possible implementation manner, determining, row by row, whether the integration time corresponding to each row in the content hops includes: judging whether the integral time corresponding to each row in the content is less than 0 row by row; or calculating a first average integration time according to the integration time corresponding to each line of the image start absolute line count to the image end absolute line count part, and judging whether the integration time corresponding to each line is larger than a first average integration time of a designated multiple line by line.
Specifically, the first column of the IT file records absolute row count, the third column records integration time, and the L is judged row by row st To L stop Whether the integration time corresponding to the third column of each row is less than 0 or whether the integration time is greater than a specified multiple of the first average integration time, for example, a specified multiple of 2.
Referring to fig. 3, a schematic structural diagram of an imaging time data file (.it file) according to an embodiment of the present application is shown. In fig. 3, the integration time of the row count 693151 is 0.001742005, the integration time of the row count 693150 adjacent to the row count is 0.000346005, the integration time of the row count 693152 adjacent to the next row is 0.000344992, and therefore, the row integration time of the row count 693151 has a distinct jump from the adjacent row and the adjacent row. Referring to fig. 4, a schematic diagram of variation of integration time with line count is provided in an embodiment of the present application. The presence of outliers of the integration time jumps for part of the rows is evident in fig. 4.
Step 103, if jump occurs, generating early warning information, outputting the information of the image to be detected and the line count and the integration time of the jump, wherein the early warning information is used for indicating that the image to be detected is locally distorted along the track direction, and the information of the image to be detected comprises satellite identification, a receiving track number, an imaging track number, a strip number, a scene start absolute line count, a scene end absolute line count, an abnormal line count and an abnormal value of the integration time.
Specifically, if L star To L stop Generating early warning information, outputting information of the image to be detected and line counting and integrating time of jumping when any line in the lines has the jumping of integrating time; otherwise, the process ends.
Further, in order to improve the accuracy of detection, when the fact that the image to be detected is distorted along the track direction is detected, the image to be detected needs to be instructed to be manually read, and whether the image to be detected really exists along the track direction or not is further determined manually.
In one possible implementation, the method further includes: performing manual interpretation according to the early warning information indication to obtain a manual interpretation result; and generating a local distortion detection report along the track direction corresponding to the image to be detected according to the manual interpretation result and the early warning information.
Specifically, a technician extracts a tiff image of a corresponding product according to early warning information, counts the position of each scene according to abnormal lines of the early warning image, rapidly positions the position of the image where the partial distortion occurs along the track direction, obtains a manual interpretation result, and generates a partial distortion detection report corresponding to the image to be detected along the track direction according to the manual interpretation result and the early warning information.
Further, in order to normally produce the remote sensing image, correction needs to be performed on the image data detected that there is distortion along the track direction, and in one possible implementation manner, if the manual judgment result is that the image to be detected has local distortion along the track direction, the method further includes: calculating second average integration time according to the integration time recorded by each line in the imaging time data file, and determining first line count, first integration time and first line time corresponding to each line of at least one line with integration time jump in the imaging time data file; smoothing the first line between two adjacent integration time jump lines according to the first line, the first integration time and the second average integration time corresponding to the two adjacent integration time jump lines to obtain corrected line; and correcting the integration time corresponding to each line of the line with the integration time jump in the imaging time data file according to the corrected line to obtain corrected integration time.
Specifically, in the scheme provided in the embodiments of the present application, according to L in the IT file star Line to L stop Calculating second average integration time of the integration time recorded in each line of the content corresponding to the line, and then determining L star Line to L stop At least one line of the line having an integration time jump, and a first line count (L 1 ,L 2 ，，L n ) First integration time (IT (L) 1 ),IT(L 2 ),…,IT(L n ) When in the first row (LT (L) 1 ),LT(L 2 ),…,LT(L n ) And n is a positive integer not less than 1. Smoothing the first line between two adjacent integration time jump lines according to the first line, the first integration time and the second average integration time corresponding to the two adjacent integration time jump lines to obtain corrected line; and correcting the integration time corresponding to each line of the line with the integration time jump in the imaging time data file according to the corrected line to obtain corrected integration time. L in IT file star Line to L stop In the content corresponding to the rows, two adjacent rows with integration time jump exist, which may be two rows with continuous row count, or two rows with discontinuous row count, for example, two adjacent integration time jump behaviors 693151 and 693152 occur, or two adjacent integration time jump behaviors 693151 and 693526 occur.
Further, if the integration time hopping behavior occurs twice adjacently, the imaging is performedThe first and second lines of the time data file with integration time transitions, wherein the first line with integration time transitions is defined as L star Line to L stop The first line between the first and second integration time hopped lines is smoothed to obtain a corrected line by:
LT re (i)＝LT(i)-IT(L 1 )+IT mean
wherein LT re (i) Representing the row corresponding to the i-th row after correction; LT (i) represents a row corresponding to the i-th row before correction; IT (L) 1 ) Represents the L < th 1 Integration time corresponding to the row; IT (information technology) mean Representing a second average integration time; l (L) 1 +1≤i≤L 2 ，L 1 Representing a first row count, L, corresponding to the first-occurring integration time hopping row 2 Representing the first row count corresponding to the second occurrence of the integration time hopping row.
Further, if the integration time hopping lines occur in two adjacent times, the j-th and j+1th integration time hopping lines occur in the imaging time data file, j is a positive integer not less than 2, for example, the integration time hopping lines occur in the second time and the third time, or the integration time hopping lines occur in the fourth time and the fifth time, etc.
If the integration time jump behaviors occur in two adjacent times, the j-th and j+1th integration time jump lines occur in the imaging time data file, j is a positive integer not less than 2, and smoothing the first line between the integration time jump lines occurs in two adjacent times according to the first line, the first integration time and the second average integration time corresponding to the integration time jump lines in two adjacent times to obtain a corrected line, including:
and smoothing the first row between the two adjacent integration time jump rows to obtain a corrected row by the following steps:
LT re (i)＝LT(i)-(LT(L j +1)-LT re (L j ))+IT mean
wherein L is j A first row count corresponding to a row representing a j-th occurrence of an integration time jump; LT (L) j +1) represents the row corresponding to the next row in which the integration time jump occurs the j-th time; LT (LT) re (L j ) Representing a corrected row corresponding to a row in which the integration time jump occurs the j-th time; j represents the integral time jump which occurs for the j th time, and j is more than or equal to 2 and less than or equal to n; l (L) j +1≤i≤L j+1 。
Further, after correcting the integration time in the IT file, the line is also corrected. In one possible implementation manner, correcting, according to the corrected line, an integration time corresponding to each line of the imaging time data file in which the integration time jump line appears, to obtain a corrected integration time, including:
correcting the integration time corresponding to each row by the following formula to obtain corrected integration time:
IT re (i)＝LT re (i+1)-LT re (i)
wherein IT is re (i) Indicating the integration time after the correction of the ith row; LT (LT) re (i+1) represents a corrected row of the i+1th row; LT (LT) re (i) Representing the row after the i-th row is corrected; i=l 1， L 2 ,,L n ，L 1, L 2 ，， n Representing the first row count.
Further, in one possible implementation manner, the method further includes: and obtaining a new imaging time data file according to the corrected row and the corrected integration time, and regenerating a new remote sensing image according to the new imaging time data file.
Specifically, the new imaging time data file is replaced with the original IT file of the production system, and the strip image is reordered to produce a new image without distortion.
Further, in one possible implementation manner, the method further includes: performing geometric positioning accuracy detection on the new remote sensing image; if the geometric positioning accuracy exceeds the limit, performing rational polynomial coefficient (Rational Polynomial Coefficients, RPC) refinement on the new remote sensing image according to the preset reference image to obtain an image meeting the preset geometric positioning accuracy requirement.
Further, in order to verify the detection effect of the local distortion of the remote sensing image along the track direction provided by the application, the effect of the scheme provided by the embodiment of the application is described below by taking a first satellite with a high scene as an example.
For example, the image data to be inspected is all product image data of 11 months of the 11 th year of the high scene, four stars.
1) Verification method
(1) The technology provided by the embodiment of the application, the prior art 1, the prior art 2, the prior art 3 and the manual visual interpretation method are adopted to respectively carry out local distortion along the track direction on the verification data, and the accuracy and timeliness of the verification data are compared and analyzed, wherein the prior art 1 refers to automatic registration and splicing of remote sensing images based on SIFI characteristics, the prior art 2 refers to high-performance remote sensing image registration based on mutual information, and the prior art 3 refers to heterogeneous image matching based on phase consistency.
(2) The technology provided by the embodiment of the application is adopted to correct the image with the local distortion in the track direction, and the correction effect is verified by comparing the image with the image before correction.
2) Verification result
(1) The results of the detection of local distortion in the track direction of the 11 month image product in the year 2020 of the first four stars of the landscape by adopting the technology, the prior art 1, the prior art 2, the prior art 3 and the artificial visual interpretation method provided by the embodiment of the application are as follows: table 1 shows the output result of detecting the local distortion of the image product of the first four stars and 11 months in the first scene along the track direction by the detection method based on the technology provided by the embodiment of the application; table 2 shows comparison of local distortion detection results of the 11 month image product in the track direction in the year 2020 with the four stars of the first scene based on the technology provided by the embodiment of the present application and the detection method of the prior art.
TABLE 1
TABLE 2
(2) The influence of local distortion in the track direction detected by the technology provided by the embodiment of the application is corrected, geometric positioning accuracy detection is carried out on the image according to the reference image, and RPC refinement processing is carried out on the image if the accuracy overruns. Taking images with scene numbers 2900681 and 2900694 as examples, see table 3 for information of images with scene number 2900681, and see table 4 for information of images with scene number 2900694, the comparison results before and after correction are shown in fig. 5a and 5b, and fig. 6a and 6 b.
TABLE 3 Table 3
TABLE 4 Table 4
3) The analysis and conclusion of the verification result are based on the verification result, and the conclusion is as follows:
(1) The ground data processing system of the first satellite of the high scene produces about 300 scene image products each day, if all products need to be detected by manual visual inspection to locally distort along the track direction, each scene needs about 1 minute, and 300 scenes need 300 minutes; if the prior art 1, 2 and 3 are used for detecting the local distortion along the track direction, the calculation amount is large, each scene needs about 2 minutes, 1.8 minutes and 1.7 minutes respectively, and the total time of 300 scenes is 600 minutes, 540 minutes and 510 minutes respectively; the method for detecting the local distortion of the image along the track direction based on the technology of the application needs about 0.027 minute for each scene, and only 8 minutes for 300 scene detection, so that manpower, time and software and hardware resources are greatly saved, and timeliness of data delivery is guaranteed.
(2) Human eyes judge to detect the condition that local distortion of satellite images along the track direction is extremely easy to cause missing error detection and detection, 3 scenes are missed to detect in 11 months of data, the accuracy is 20% in the wrong detection of 13 scenes, and 5 scenes in the wrong detection of 13 scenes are wrong detection caused by sawtooth-shaped textures or edges at certain angles caused by repeated gray level resampling; 8 scenes are false detection caused by ground texture or edge diffusion, color stripes and the like after bad pixel interpolation processing. Detecting local distortion of an image along the track direction by using the prior art 1, 2 and 3, and respectively performing error detection on 4 scenes, 6 scenes and 6 scenes, and performing missed detection on 3 scenes, 2 scenes and 4 scenes, wherein the accuracy rates are 65%, 60% and 50% respectively, the above error detected scenes are erroneous judgment caused by failure of matching the same-name points of the weak texture images, and the missed detection scenes are missed detection caused by the fact that the same-name feature points are not in the line number range with local band dislocation; the detection image based on the technology of the application has the advantages that the problem of local distortion along the track direction of the detection image is solved, no false detection and no missing detection occur, the accuracy can reach 100%, the accuracy is greatly improved, and the quality of data delivery is ensured.
(3) The image with the local distortion along the track direction detected is corrected by the application technology, the distortion disappears, and the image can be smoothly delivered to users for use, so that the complaint rate of the users is greatly reduced, the satisfaction degree of the users is improved, and the waste of satellite and ground resources is reduced.
Therefore, in the scheme provided by the embodiment of the application, on one hand, the problems of high image number, low detection efficiency and accuracy caused by manual detection are avoided through an automatic detection mode, and on the other hand, the problems of automatic matching precision and false detection caused by the fact that in the detection process of automatically matching images by acquiring homonymous feature points through SIFT algorithm, mutual information or phase consistency algorithm and the like, a plurality of gray resampling or single texture of water and desert images and finer texture of farmland and woodland images are possibly carried out in the geometric processing process of bad pixels and image radiation of each wave band of a camera are avoided.
According to the scheme provided by the embodiment of the application, the scene number, the image start absolute line count and the image end absolute line count of the image to be detected are extracted from the meta-information file, the integration time recorded by each line of the image start absolute line count to the image end absolute line count part is obtained from the imaging time data file according to the scene number, whether the integration time corresponding to each line is jumped or not is judged line by line, the fact that the image possibly has local distortion along the track direction is determined according to the jump of the integration time, the phenomenon that the texture of the water body and the desert image is single, the texture of the farmland and woodland image is finer is broken, the accuracy of automatic matching is affected, the detection is easy to miss, and the detection accuracy is low is avoided.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (7)
1. A method for rapidly detecting and correcting local distortion of a remote sensing image along a track direction is characterized by comprising the following steps:
acquiring an imaging time data file and a meta information file of an image to be detected, and extracting information of the image to be detected from the meta information file, wherein the information of the image to be detected comprises a scene number, an image start absolute line count and an image end absolute line count of the image to be detected;
acquiring integration time recorded by each line of an image starting absolute line count-image ending absolute line count part from an imaging time data file according to the scene number, and judging whether jump occurs in the integration time corresponding to each line by line;
if jump occurs, generating early warning information, outputting information of the image to be detected and line counting and integration time of the jump, wherein the early warning information is used for indicating that the image to be detected is locally distorted along the track direction;
performing manual interpretation according to the early warning information indication to obtain a manual interpretation result;
generating a local distortion detection report in the track direction corresponding to the image to be detected according to the manual interpretation result and the early warning information;
if the manual judgment result is that the image to be detected has local distortion along the track direction, the method further comprises the following steps:
calculating second average integration time according to the integration time recorded by each line in the imaging time data file, and determining first line count, first integration time and first line time corresponding to each line of at least one line with integration time jump in the imaging time data file;
smoothing the first line between two adjacent integration time jump lines according to the first line, the first integration time and the second average integration time corresponding to the two adjacent integration time jump lines to obtain corrected line;
and correcting the integration time corresponding to each line of the line with the integration time jump in the imaging time data file according to the corrected line to obtain corrected integration time.
2. The method of claim 1, wherein determining, row by row, whether the integration time corresponding to each row transitions comprises:
judging whether the integration time corresponding to each row is smaller than 0 row by row; or (b)
And calculating first average integration time according to the integration time corresponding to each line of the image starting absolute line counting part to the image ending absolute line counting part, and judging whether the integration time corresponding to each line is larger than the first average integration time of a designated multiple line by line.
3. The method of claim 1, wherein if the integration time hopping lines occur for the first time and the second time in the imaging time data file in the adjacent two times when the integration time hopping lines occur, smoothing the first line between the adjacent two times when the integration time hopping lines occur according to the first line, the first integration time, and the second average integration time corresponding to the adjacent two times when the integration time hopping lines occur, to obtain corrected lines, comprising:
and smoothing the first row between the two adjacent integration time jump rows to obtain a corrected row by the following steps:
LT re (i)＝LT(i)-IT(L 1 )+IT mean
wherein LT re (i) Representing the row corresponding to the i-th row after correction; LT (i) represents a row corresponding to the i-th row before correction; IT (L) 1 ) Represents the L < th 1 Integration time corresponding to the row; IT (information technology) mean Representing a second average integration time; l (L) 1 +1≤i≤L 2 ，L 1 Representing a first row count, L, corresponding to the first-occurring integration time hopping row 2 Representing the first row count corresponding to the second occurrence of the integration time hopping row.
4. The method of claim 3, wherein if the j-th and j+1th integration time hopping lines in the imaging time data file occur in two adjacent integration time hopping lines, j is a positive integer not less than 2, smoothing the first line between the two adjacent integration time hopping lines according to the first line, the first integration time, and the second average integration time corresponding to the two adjacent integration time hopping lines, to obtain a corrected line, comprising:
and smoothing the first row between the two adjacent integration time jump rows to obtain a corrected row by the following steps:
LT re (i)＝LT(i)-(LT(L j +1)-LT re (L j ))+IT mean
wherein L is j A first row count corresponding to a row representing a j-th occurrence of an integration time jump; LT (L) j +1) represents the row corresponding to the next row in which the integration time jump occurs the j-th time; LT (LT) re (L j ) Representing a corrected row corresponding to a row in which the integration time jump occurs the j-th time; j represents the integral time jump which occurs for the j th time, and j is more than or equal to 2 and less than or equal to n; l (L) j +1≤i≤L j+1 。
5. The method of claim 4, wherein correcting the integration time corresponding to each line of the imaging time data file in which the integration time jump line occurs according to the corrected line comprises:
correcting the integration time corresponding to each row by the following formula to obtain corrected integration time:
IT re (i)＝LT re (i+1)-LT re (i)
wherein IT is re (i) Indicating the integration time after the correction of the ith row; LT (LT) re (i+1) represents a corrected row of the i+1th row; LT (LT) re (i) Representing the row after the i-th row is corrected; i=l 1, L 2 ,…,L n ，L 1, L 2 ,…,L n Representing the first row count.
6. The method of any one of claims 3 to 5, further comprising:
and obtaining a new imaging time data file according to the corrected row and the corrected integration time, and regenerating a new remote sensing image according to the new imaging time data file.
7. The method as recited in claim 6, further comprising:
performing geometric positioning accuracy detection on the new remote sensing image;
and if the geometric positioning accuracy exceeds the limit, performing RPC refinement on the new remote sensing image according to a preset reference image to obtain an image meeting the requirement of the preset geometric positioning accuracy.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5072226A (en) * | 1990-06-07 | 1991-12-10 | Hughes Aircraft Company | Radiometer system incorporating a cylindrical parabolic reflector and minimum redundancy array feed |
| CN101827223A (en) * | 2010-04-20 | 2010-09-08 | 武汉大学 | Inner field stitching method of non-collinear TDI CCD imaging data based on line frequency normalization |
| CN103914808A (en) * | 2014-03-14 | 2014-07-09 | 国家测绘地理信息局卫星测绘应用中心 | Method for splicing ZY3 satellite three-line-scanner image and multispectral image |
| CN103968808A (en) * | 2013-01-24 | 2014-08-06 | 国家基础地理信息中心 | Strict geometric correction method for wide-field satellite CCD images |
| CN112327334A (en) * | 2020-09-29 | 2021-02-05 | 航天恒星科技有限公司 | Low-earth-orbit satellite-assisted GNSS long code signal capturing method and device |
-
2021
- 2021-07-15 CN CN202110801663.3A patent/CN113643240B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5072226A (en) * | 1990-06-07 | 1991-12-10 | Hughes Aircraft Company | Radiometer system incorporating a cylindrical parabolic reflector and minimum redundancy array feed |
| CN101827223A (en) * | 2010-04-20 | 2010-09-08 | 武汉大学 | Inner field stitching method of non-collinear TDI CCD imaging data based on line frequency normalization |
| CN103968808A (en) * | 2013-01-24 | 2014-08-06 | 国家基础地理信息中心 | Strict geometric correction method for wide-field satellite CCD images |
| CN103914808A (en) * | 2014-03-14 | 2014-07-09 | 国家测绘地理信息局卫星测绘应用中心 | Method for splicing ZY3 satellite three-line-scanner image and multispectral image |
| CN112327334A (en) * | 2020-09-29 | 2021-02-05 | 航天恒星科技有限公司 | Low-earth-orbit satellite-assisted GNSS long code signal capturing method and device |
Non-Patent Citations (4)
| Title |
|---|
| A NOVEL STICTCHING PRODUCT CREATING ALGORITHM BASED ON SECTIONAL RPC FITTING;MENG Weican et.al;《ResearchGate》;第1-15页 * |
| TDI 行积分时间跳变对视差的影响及纠正方法;曹彬才 等;《测绘科学技术学报》;第32卷(第6期);第611-614页 * |
| 卫星遥感影像处理系统浏览图生成与云掩膜生成模块的设计与实现;李妍;《中国优秀硕士学位论文全文数据库 信息科技辑》(第10期);论文第21-22页 * |
| 资源三号测绘卫星传感器校正产品生产方法研究;唐新明 等;《武汉大学学报 信息科学版》;第39卷(第3期);第287-299页 * |
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