CN114529831A - Satellite remote sensing image irregular scattered point on-satellite resampling processing algorithm and system - Google Patents
Satellite remote sensing image irregular scattered point on-satellite resampling processing algorithm and system Download PDFInfo
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Abstract
The invention provides an irregular scattered point on-satellite resampling processing algorithm and system for a satellite remote sensing image, which are used for acquiring satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values and calculating imaging time; acquiring geographic position coordinates which correspond to remote sensing image data pixels one by one; converting the uncalibrated remote sensing image into calibrated image data with physical significance and dimension; recording the imaging time, the calibrated remote sensing data and the geographical position information into a cache; determining a required irregular scattered point row number index range, and releasing an irregular scattered point storage space which is no longer required in a cache; determining the column index range of the irregular scattered points required by each new point; calculating the square of the space distance between the new point and the irregular point; screening out a plurality of irregular discrete points which are close to the new point; and calculating and outputting a physical value corresponding to the new point position. The method has simple structure, is easy to realize on the satellite, and can be generally applied to on-satellite resampling processing of irregular scattered points.
Description
Technical Field
The invention relates to the technical field of on-satellite data, in particular to an irregular scattered point on-satellite resampling processing algorithm and system for a satellite remote sensing image.
Background
With the development of remote sensing technology, the real-time performance and the high efficiency of satellite information acquisition by remote sensing satellite users are higher and higher, so that information processing, fusion and extraction on the satellite become necessary. On-board processing may involve different approaches for different purposes. For imaging remote sensing satellites, how to resample is one of the common problems in the field of remote sensing image processing. For the resampling of regular grid points, the existing projection and interpolation methods can be applied. For the satellite remote sensing image, the sampling point is influenced by factors such as a load imaging mode, the earth curvature, the satellite attitude, the satellite vibration and the like, and irregular sampling is often shown. In order to adapt to processing such as multi-source load matching, channel registration, image distortion correction and the like in on-satellite processing, a method suitable for irregular scattered point on-satellite resampling of a satellite remote sensing image needs to be researched.
An existing document [1] (research on irregular sampling image restoration algorithm in Zhangiang, space remote sensing, Harbin industry university, 2012) provides a method for image quality improvement of irregular sampling caused by platform vibration, wherein the method is based on a convolution degradation model and does not involve resampling. The existing literature [2] (leaf seal, qiu xu min, huang courage, etc., meteorological remote sensing image and grid field resampling interpolation method, computer engineering and application, 2013, volume 49, 18 th) provides a method for resampling regular grid point (grid) data of a remote sensing image in a ground application system, which is not suitable for resampling of irregular points. In the existing document [3] (wanowl house, xiaoqing, yang lycopi nane, remote sensing image resampling method implementation and application research, software, 2019, volume 40, stage 7), the image is rotated and scaled by two resampling methods, namely a nearest interpolation method and a bilinear interpolation method, and the method is only suitable for processing regular sampling points. The existing literature [4] (building Xiu lin, huangweggong, zhongbao, etc., a fast algorithm for resampling remote sensing image data, the remote sensing academic newspaper, volume 6, phase 2, 2002) provides a method for performing geometric distortion correction resampling on the ground, the resampling is established on the basis of the positioning of resampling conjugate points on the basis of longitude and latitude data obtained by geographic positioning, and a half-and-half lookup method is required to search. The existing methods cannot be applied to the on-satellite resampling processing of irregular scattered points of the satellite remote sensing image.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an irregular scattered point on-satellite resampling processing algorithm and system for a satellite remote sensing image.
The invention provides an irregular scattered point on-satellite resampling processing algorithm for a satellite remote sensing image, which comprises the following steps:
step 1: the on-board processing module receives a satellite original remote sensing data packet, analyzes the remote sensing packet, obtains satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time;
step 2: carrying out point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters in the step 1, and acquiring geographic position coordinates which correspond to the pixels of the remote sensing image data one by one;
and step 3: scaling calculation is carried out on the geographic position coordinates in the step 2 according to the absolute scaling coefficient, and the uncalibrated remote sensing image is converted into calibrated remote sensing image data with physical significance and dimension;
and 4, step 4: recording the imaging time, the calibrated remote sensing image data and the geographical position information in the step 1-3 into a cache;
and 5: while continuously calculating and storing process data into a cache, calculating the time and the geographic position of a new point, comparing the imaging time of the new point with the imaging time of an original image, determining the required line number index range of irregular scattered points, and releasing the irregular scattered point storage space which is not required in the cache any more;
step 6: determining the column index range of the irregular scattered points required by each new point according to the relation between the index value and the irregular scattered point column number of the new point with the same time;
and 7: for each new point, reading the physical values and the geographical position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point;
and 8: screening out a plurality of irregular discrete points close to the new point through threshold comparison;
and step 9: calculating and outputting a physical value corresponding to the new point position;
step 10: and repeating the processing of the step 5 to the step 9 for a new point of the next time period.
Preferably, in the step 5, the calculating the new point geographic position specifically includes: and calculating the geographical position of the new point according to the time of the new point, the visual axis vector of the satellite orbit system corresponding to the new point and the earth ellipsoid model parameters.
Preferably, the calculating the new point geographic location specifically includes:
if at a certain time t, the visual axis vector of the new point in the satellite orbit system is normalized to a unit vector, and the visual axis vector of the new point in the satellite orbit system is
i represents the i-th new point index value corresponding to the same time t, corresponding to the time tConverting the satellite orbit system to the earth center rotation coordinate system into a conversion matrix of T, and obtaining a new point visual axis vector under the earth center rotation coordinate system
The intersection point of the new point position coordinate and the earth ellipsoid model is the new point position coordinate, wherein
Preferably, in the step 5, the onboard processing module calculates a time difference between the new point and the original imaging, the line number index range is determined by a corresponding relationship between the time difference and the relative line number index range, and the corresponding relationship is calculated in advance on the ground to obtain a result, which is stored in the onboard processing module.
Preferably, in step 6, the on-board processing module obtains the column index of the irregular point according to the new point index value i, and the corresponding relationship is calculated in advance by the ground to obtain a result, which is stored in the on-board processing module.
Preferably, in step 7, if the coordinates of the new point position are
The coordinates of a certain irregular discrete point are
The square d of the spatial distance is then expressed as:
d＝(a-α)2+(b-β)2+(c-γ)2。
preferably, in step 8, the threshold is determined by an average of the densities of the original irregular points at the new point position.
Preferably, in step 8, the density of the irregular points is defined as the number of the irregular points possibly existing in the unit area, if the original irregular points are not uniformly distributed, different thresholds are applied to each new point position, and the thresholds are calculated in advance by the ground to obtain a result, and are stored in the on-satellite processing module; and setting the screened irregular discrete point upper limit value N, and finishing threshold comparison after N irregular discrete points smaller than the threshold are obtained when threshold comparison is sequentially performed in a possible index range.
Preferably, in step 9, the method for calculating the physical value corresponding to the new point position is a weighting coefficient method, and if M irregular discrete points are screened out in step 8, the square of the distances between the M irregular discrete points and the new point is d in sequencejWhere j is 1,2, …, M, and their corresponding calibrated physical values are fjWhere j is 1,2, …, M, the calculation formula of the physical value g corresponding to the new point position is:
the invention also provides a system for resampling on the irregular scattered point satellite of the satellite remote sensing image, which comprises the following modules:
an imaging time calculation module: the on-board processing module receives a satellite original remote sensing data packet, analyzes the remote sensing packet, obtains satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time;
a geographic location coordinate acquisition module: performing point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters, and acquiring geographic position coordinates which are one-by-one corresponding to pixels of the remote sensing image data, namely irregular scattered point three-dimensional position coordinates;
the remote sensing image conversion module: scaling calculation is carried out according to the absolute scaling coefficient, and the uncalibrated remote sensing image is converted into calibrated image data with physical significance and dimension;
a cache module: recording the imaging time, the calibrated remote sensing data and the geographical position information into a cache;
an irregular scattered point row number index range determining module: while continuously calculating and storing process data into a cache, calculating new point time and geographic position, comparing the new point time with the original image imaging time, determining a required irregular scattered point line number index range, and releasing an irregular scattered point storage space which is not required in the cache any more;
the column index range determining module of the irregular scattered points required by the new point comprises the following steps: determining the column index range of the irregular scattered points required by each new point according to the relation between the index value and the irregular scattered point column number of the new point with the same time;
the space distance calculation module between the new point and the irregular point: for each new point, reading the physical values and the geographical position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point;
an irregular discrete point screening module: screening out a plurality of irregular discrete points close to the new point through threshold comparison;
a new point position calculation module: calculating and outputting a physical value corresponding to the new point position;
a circulation module: and for a new point in the next time period, repeating the processing of the irregular scattered point row number index range determining module, the irregular scattered point column number index range determining module required by the new point, the spatial distance calculating module between the new point and the irregular point and the new point position calculating module.
Compared with the prior art, the invention has the following beneficial effects:
1. the method has simple structure, is easy to realize on the satellite, and can be generally applied to on-satellite resampling treatment of irregular scattered points;
2. the invention can realize the resampling of three-dimensional discrete points on the satellite;
3. the invention does not need to carry out triangularization grid construction of discrete points on the satellite.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a geographic location (showing a 15 row by 15 column area) corresponding to image data acquired within an imaging time period of a remote sensing load;
FIG. 3 is raw image data over a period of time for a certain satellite load;
FIG. 4 shows the geographic location point corresponding to each pixel of FIG. 3;
FIG. 5 shows a plurality of new point locations and original irregular scattered point locations matched with time at the same time;
FIG. 6 is a diagram of an original irregular scatter plot screened out at a new point;
fig. 7 is an image physical value obtained by resampling the original image data shown in fig. 3.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in FIG. 1, the algorithm for resampling on irregular scattered points and satellites of the satellite remote sensing image provided by the invention comprises the following steps:
step 1: and the on-board processing module receives the satellite original remote sensing data packet, analyzes the remote sensing packet, acquires satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time.
Step 2: and (3) carrying out point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters in the step (1), and acquiring geographic position coordinates which correspond to the pixels of the remote sensing image data one by one, namely irregular scattered point three-dimensional position coordinates.
And step 3: and (3) carrying out calibration calculation on the geographic position coordinates in the step (2) according to the absolute calibration coefficient, and converting the uncalibrated remote sensing image into calibrated remote sensing image data with physical significance and dimension.
And 4, step 4: and (4) recording the imaging time, the calibrated remote sensing image data and the geographical position information in the step (1-3) into a cache.
And 5: while continuously calculating and storing the process data into the cache, calculating the time and the geographic position of a new point, and comparing the imaging time of the new point with the original imageDetermining the required irregular scattered point row number index range according to the imaging time, releasing the irregular scattered point storage space which is no longer required in the cache, and calculating the geographical position of a new point in step 5 specifically comprises the following steps: calculating the geographic position of the new point according to the time of the new point, the visual axis vector of the new point in the satellite orbit system and the earth ellipsoid model parameters, normalizing the visual axis vector of the new point in the satellite orbit system to be a unit vector at a certain time t, and obtaining the visual axis vector of the new point in the satellite orbit system
i represents the index value of the ith new point corresponding to the same time T, the conversion matrix from the satellite orbit system to the geocentric rotation coordinate system is T corresponding to the time T, and the visual axis vector of the new point under the geocentric rotation coordinate system
The intersection point of the new point position coordinate and the earth ellipsoid model is the new point position coordinate, wherein
A common irregular resampling new point position is a new point distributed along the direction of the track and the vertical track, in step 5, the on-board processing module calculates the time difference between the new point and the original imaging time difference, the line index range is determined by the corresponding relation between the time difference and the relative line index range, the corresponding relation is calculated in advance by the ground to obtain a result, and the result is stored in the on-board processing module.
Step 6: and 6, determining the column index range of the irregular scattered points required by each new point according to the relationship between the index value and the irregular scattered point column number of the new point with the same time, acquiring the column index of the irregular points by the on-board processing module according to the index value i of the new point, calculating the corresponding relationship in advance by the ground to obtain a result, and storing the result in the on-board processing module.
And 7: for each new point, reading the physical values and the geographic position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point, in step 7, if the position coordinates of the new point are the
The coordinates of a non-regular discrete point are
Then the square of the spatial distance, d, is expressed as:
d＝(a-α)2+(b-β)2+(c-γ)2。
and step 8: screening out a plurality of irregular discrete points close to the new point through threshold comparison, in step 8, determining the threshold value through the density average value of the original irregular points at the position of the new point, in step 8, defining the density of the irregular points as the number of the irregular points possibly existing on a unit area, if the original irregular points are not uniformly distributed, applying different threshold values to each new point position, calculating the threshold value in advance by the ground to obtain a result, storing the result in an on-satellite processing module, in step 8, setting the upper limit value N of the screened irregular discrete points, and finishing the threshold comparison after N irregular discrete points smaller than the threshold value are obtained when the threshold value comparison is sequentially carried out in a possible index range, so that the running time is accelerated and the operation resources are saved.
And step 9: calculating and outputting a physical value corresponding to the new point position, wherein in the step 9, the calculation method of the physical value corresponding to the new point position is a weighting coefficient method, and if M irregular discrete points are screened out in the step 8, the squares of the distances between the irregular discrete points and the new point are d in sequencejWhere j is 1,2, …, M, and their corresponding calibrated physical values are fjWhere j is 1,2, …, M, and the calculation formula of the physical value g corresponding to the new point position is:
step 10: and repeating the processing of the step 5 to the step 9 for a new point of the next time period.
The invention also provides a system for resampling on the irregular scattered point satellite of the satellite remote sensing image, which comprises the following modules:
an imaging time calculation module: the on-board processing module receives a satellite original remote sensing data packet, analyzes the remote sensing packet, obtains satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time;
a geographic location coordinate acquisition module: performing point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters, and acquiring geographic position coordinates which are one-by-one corresponding to pixels of the remote sensing image data, namely irregular scattered point three-dimensional position coordinates;
the remote sensing image conversion module: scaling calculation is carried out according to the absolute scaling coefficient, and the uncalibrated remote sensing image is converted into calibrated image data with physical significance and dimension;
a cache module: recording the imaging time, the calibrated remote sensing data and the geographical position information into a cache;
an irregular scattered point row number index range determining module: while continuously calculating and storing process data into a cache, calculating new point time and geographic position, comparing the new point time with the original image imaging time, determining a required irregular scattered point line number index range, and releasing an irregular scattered point storage space which is not required in the cache any more;
the column number index range determining module of the irregular scatter required by the new point comprises the following steps: determining the column index range of the irregular scattered points required by each new point according to the relation between the index value and the irregular scattered point column number of the new point with the same time;
the space distance calculation module between the new point and the irregular point: for each new point, reading the physical values and the geographical position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point;
an irregular discrete point screening module: screening out a plurality of irregular discrete points close to the new point through threshold comparison;
a new point position calculation module: calculating and outputting a physical value corresponding to the new point position;
a circulation module: and for a new point in the next time period, repeating the processing of the irregular scattered point row number index range determining module, the irregular scattered point column number index range determining module required by the new point, the spatial distance calculating module between the new point and the irregular point and the new point position calculating module.
Preferred embodiment(s) of the invention：
The objective of satellite remote sensing image irregular scattered point on-satellite resampling is to realize mapping of irregular three-dimensional space discrete points obtained by pixel-by-pixel positioning on a satellite to another three-dimensional space discrete position points, and the realization process is shown in fig. 1.
Firstly, an on-board processing module receives a satellite original remote sensing data packet, analyzes the remote sensing packet, obtains satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time.
And carrying out point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters, and acquiring geographic position coordinates (namely irregular scattered point three-dimensional position coordinates) which are one-by-one corresponding to the pixels of the remote sensing image data. If the number of lines of the image data is K within a certain time periodrowsThe number of rows is KcolumnsGeographical position coordinates generated after point-by-point geographical positioning
Wherein m and n are row and column indexes of corresponding image data respectively, and the value range of m is as follows: 1,2, …, KrowsAnd n is in a value range: 1,2, …, Kcolumns. Geographic position coordinates corresponding to remote sensing image data pixels within a period of time are distributed in a three-dimensional space of a geocentric rotation coordinate system, and due to the influence of factors such as a remote sensing load imaging mode, a satellite attitude, earth curvature and the like, the distribution of the position points in the geocentric rotation coordinate system is irregular. Fig. 2 shows the geographical position corresponding to the image data acquired in an imaging period of a certain remote sensing load (the position points of 15 rows and 15 columns are shown in the figure).
Before resampling calculation, the uncalibrated remote sensing image needs to be converted into calibrated image data with physical significance and dimension, so that calibration calculation needs to be performed according to an absolute calibration coefficient.
And after the calculation is finished, recording the imaging time, the calibrated remote sensing data and the geographical position information into a cache.
And continuously calculating and storing process data into the cache, calculating the time and the geographic position of a new point, comparing the time of the new point with the imaging time of the original image, determining the required line number index range of the irregular scattered points, and releasing the irregular scattered point storage space which is not required in the cache any more.
And calculating the geographical position of the new point according to the time of the new point, the visual axis vector of the satellite orbit system corresponding to the new point and the earth ellipsoid model parameters. If at a certain time t, the new point's visual axis vector (normalized to unit vector) under the satellite orbit is
(i represents the index value of the ith new point corresponding to the same time T), and the conversion matrix from the satellite orbit system to the geocentric rotation coordinate system corresponding to the time T is T, so that the visual axis vector of the new point under the geocentric rotation coordinate system
And the intersection point of the new point position coordinate and the earth ellipsoid model is the new point position coordinate. A common irregular resampling new point position is a new point distributed along the track and the vertical track, respectively.
The original remote sensing image is obtained according to a certain period, the new points are also obtained according to a fixed period, the periods of the original remote sensing image and the new points may be different, and a certain time deviation may exist between the two points. The on-board processing module calculates the time difference between the new point and the original imaging, the line number index range is determined by the corresponding relation between the time difference and the relative line number index range, and the corresponding relation is calculated in advance by the ground to obtain a result which is stored in the on-board processing module.
And for new points with the same time, the on-satellite processing module acquires the column number index of the irregular points according to the new point index value i, and the corresponding relation is calculated in advance by the ground to obtain a result which is stored in the on-satellite processing module. For the new points distributed along the direction of the track and the vertical track, the new points at the ith index position can only be distributed in partial areas of the original irregular points, and according to the corresponding relation, the range of the column index can be reduced, and the operation amount is reduced.
And for each new point, reading the physical values and the geographical position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point.
If the new point position coordinates are
The coordinates of a certain irregular discrete point are
Then the square of the spatial distance d is expressed as
d＝(a-α)2+(b-β)2+(c-γ)2(formula 1)
When resampling calculation is carried out, a plurality of irregular discrete points which are close to a new point can be screened out through threshold value comparison. The threshold is determined by the average of the densities of the original non-regular points at the new point location. The irregular dot density is defined as the number of possible irregular dots per unit area. If the original irregular points are not uniformly distributed, different thresholds are applied to each new point position. The threshold value is calculated in advance from the ground to obtain a result, and the result is stored in an on-satellite processing module.
In order to accelerate the running time and save the operation resources, the upper limit value N of the screened irregular discrete points can be set, namely when threshold comparison is sequentially carried out in a possible index range, the threshold comparison is finished after N irregular discrete points smaller than the threshold are obtained.
If a new point is selected, M irregular discrete points are screened out in total, and the square of the distance between the irregular discrete points and the new point is dj(j ═ 1,2, …, M), which correspond to calibrated physical values in the order fj(j ═ 1,2, …, M), the physical value g for the new point location is calculated as
After the physical value at the new point of one time period is calculated, the new point of the next time period can be obtained in the same way.
The method of the present invention is verified by combining a certain satellite remote sensing image, and fig. 3 and 4 are respectively raw image data of a certain satellite load within a period of time and a geographic positioning point corresponding to each pixel. The data in fig. 3 has a periodic spin-like appearance due to the way the load operates.
Fig. 5 shows the positions of a plurality of new points and the original irregular scattered points at the same time, which are distributed along the direction of the track and the vertical track and matched with time. The original irregular scatter point screened out at a new point position obtained by comparing the preset row and column index ranges with the threshold is shown in fig. 6.
The repeated operation results in the physical value (unit: K) of the image obtained by resampling the original image data shown in fig. 3, as shown in fig. 7. As can be seen from fig. 7, resampling to new point positions distributed along the along-track and vertical-track directions eliminates the image rotation in the original image. And the method adopts the same algorithm for each new point position, is suitable for on-satellite parallel processing, and has a simple algorithm framework and easy realization.
The method has simple structure, is easy to realize on the satellite, and can be generally applied to on-satellite resampling processing of irregular scattered points.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An irregular scattered point on-satellite resampling processing algorithm for a satellite remote sensing image is characterized by comprising the following steps:
step 1: the on-board processing module receives a satellite original remote sensing data packet, analyzes the remote sensing packet, obtains satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time;
and 2, step: carrying out point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters in the step 1, and acquiring geographic position coordinates which correspond to the pixels of the remote sensing image data one by one;
and step 3: scaling calculation is carried out on the geographic position coordinates in the step 2 according to the absolute scaling coefficient, and the uncalibrated remote sensing image is converted into calibrated remote sensing image data with physical significance and dimension;
and 4, step 4: recording the imaging time, the calibrated remote sensing image data and the geographical position information in the step 1-3 into a cache;
and 5: while continuously calculating and storing process data into a cache, calculating the time and the geographic position of a new point, comparing the imaging time of the new point with the imaging time of an original image, determining the required line number index range of irregular scattered points, and releasing the irregular scattered point storage space which is not required in the cache any more;
step 6: determining the column index range of the irregular scattered points required by each new point according to the relation between the index value and the irregular scattered point column number of the new point with the same time;
and 7: for each new point, reading the physical values and the geographical position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point;
and 8: screening out a plurality of irregular discrete points close to the new point through threshold comparison;
and step 9: calculating and outputting a physical value corresponding to the new point position;
step 10: and repeating the processing of the step 5 to the step 9 for a new point of the next time period.
2. The algorithm for processing the satellite remote sensing image irregular scattered point on-satellite resampling according to claim 1, wherein in the step 5, the calculating the geographical position of the new point specifically comprises: and calculating the geographical position of the new point according to the time of the new point, the visual axis vector of the satellite orbit system corresponding to the new point and the earth ellipsoid model parameters.
3. The algorithm for processing the satellite remote sensing image irregular scattered point on-satellite resampling according to claim 2, wherein the calculation of the geographical position of the new point is specifically as follows:
if at a certain time t, the visual axis vector of the new point in the satellite orbit system is normalized to a unit vector, and the visual axis vector of the new point in the satellite orbit system is
i represents the index value of the ith new point corresponding to the same time T, the conversion matrix from the satellite orbit system to the geocentric rotation coordinate system is T corresponding to the time T, and the visual axis vector of the new point under the geocentric rotation coordinate system
The intersection point of the new point position coordinate and the earth ellipsoid model is the new point position coordinate, wherein
4. The algorithm for processing the satellite remote sensing image irregular scattered point on-satellite resampling as claimed in claim 1, wherein in the step 5, the on-satellite processing module calculates the time difference between the new point and the original imaging, the line index range is determined by the corresponding relation between the time difference and the relative line index range, and the corresponding relation is calculated in advance by the ground to obtain the result, which is stored in the on-satellite processing module.
5. The algorithm for processing the satellite remote sensing image irregular scattered point on-satellite resampling according to claim 1, wherein in the step 6, the on-satellite processing module obtains the column index of the irregular point according to the new point index value i, and the corresponding relation is obtained by ground advanced calculation and stored in the on-satellite processing module.
6. The algorithm for processing the irregular scattered point on-satellite resampling on the satellite remote sensing image as claimed in claim 1, wherein in the step 7, if the coordinates of the new point position is
The coordinates of a certain irregular discrete point are
The square d of the spatial distance is then expressed as:
d＝(a-α)2+(b-β)2+(c-γ)2。
7. the algorithm for processing the satellite remote sensing image irregular scattered point on-satellite resampling according to claim 1, wherein in the step 8, the threshold value is determined by an average value of densities of original irregular points at the position of the new point.
8. The algorithm for processing the satellite remote sensing image irregular scattered point on-satellite resampling according to claim 1, wherein in the step 8, the density of the irregular points is defined as the number of the irregular points possibly existing in a unit area; if the original irregular points are not uniformly distributed, different thresholds are applied to the positions of the new points, and the thresholds are calculated in advance by the ground to obtain results, and the results are stored in an on-satellite processing module; and setting the screened irregular discrete point upper limit value N, and finishing threshold comparison after N irregular discrete points smaller than the threshold are obtained when threshold comparison is sequentially performed in a possible index range.
9. The algorithm for processing the irregular scattered point on-satellite resampling on-satellite of the satellite remote sensing image as claimed in claim 1, wherein in the step 9, the method for calculating the physical value corresponding to the new point position is a weighting coefficient method, and if M irregular discrete points are screened out in the step 8, the square of the distances between the M irregular discrete points and the new point are d in sequencejWhere j is 1,2, …, M, and their corresponding calibrated physical values are fjWhere j is 1,2, …, M, the calculation formula of the physical value g corresponding to the new point position is:
10. the system for processing the irregular scattered point on-satellite resampling of the satellite remote sensing image is characterized by comprising the following modules:
an imaging time calculation module: the on-board processing module receives a satellite original remote sensing data packet, analyzes the remote sensing packet, obtains satellite attitude parameters, satellite positioning parameters and uncalibrated remote sensing image data values, and calculates imaging time;
a geographic location coordinate acquisition module: performing point-by-point geographic positioning on the remote sensing image according to the imaging time, the satellite attitude and the positioning parameters, and acquiring geographic position coordinates which are one-by-one corresponding to pixels of the remote sensing image data, namely irregular scattered point three-dimensional position coordinates;
remote sensing image conversion module: scaling calculation is carried out according to the absolute scaling coefficient, and the uncalibrated remote sensing image is converted into calibrated image data with physical significance and dimension;
a caching module: recording the imaging time, the calibrated remote sensing data and the geographical position information into a cache;
an irregular scattered point row number index range determining module: while continuously calculating and storing process data into a cache, calculating new point time and geographical position, comparing the new point time with the original image imaging time, determining a required irregular scattered point row number index range, and releasing an irregular scattered point storage space which is not required in the cache;
the column index range determining module of the irregular scattered points required by the new point comprises the following steps: determining the column index range of the irregular scattered points required by each new point according to the relation between the index value and the irregular scattered point column number of the new point with the same time;
the space distance calculation module between the new point and the irregular point: for each new point, reading the physical values and the geographical position coordinates of the pixels in the index ranges of the rows and the columns from the cache, and calculating the square of the space distance between the new point and the irregular point;
an irregular discrete point screening module: screening out a plurality of irregular discrete points close to the new point through threshold comparison;
a new point position calculation module: calculating and outputting a physical value corresponding to the new point position;
a circulation module: and for a new point in the next time period, repeating the processing of the irregular scattered point row number index range determining module, the irregular scattered point column number index range determining module required by the new point, the spatial distance calculating module between the new point and the irregular point and the new point position calculating module.
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