CN112945384B - Data preprocessing method for multi-angle polarization satellite - Google Patents
Data preprocessing method for multi-angle polarization satellite Download PDFInfo
- Publication number
- CN112945384B CN112945384B CN202110222941.XA CN202110222941A CN112945384B CN 112945384 B CN112945384 B CN 112945384B CN 202110222941 A CN202110222941 A CN 202110222941A CN 112945384 B CN112945384 B CN 112945384B
- Authority
- CN
- China
- Prior art keywords
- data
- angle
- observation
- pixel
- processed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000007781 pre-processing Methods 0.000 title claims description 11
- 230000010287 polarization Effects 0.000 title abstract description 23
- 230000006798 recombination Effects 0.000 claims abstract description 12
- 238000005215 recombination Methods 0.000 claims abstract description 12
- 238000012163 sequencing technique Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims description 22
- 238000012360 testing method Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000000903 blocking effect Effects 0.000 abstract description 12
- 230000005855 radiation Effects 0.000 abstract description 11
- 238000005516 engineering process Methods 0.000 abstract description 6
- 230000009191 jumping Effects 0.000 abstract 2
- 238000010187 selection method Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 26
- 239000000443 aerosol Substances 0.000 description 10
- 238000003384 imaging method Methods 0.000 description 9
- 230000008521 reorganization Effects 0.000 description 9
- 230000008859 change Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 239000013598 vector Substances 0.000 description 5
- 239000002245 particle Substances 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000003595 spectral effect Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 239000008264 cloud Substances 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 235000002639 sodium chloride Nutrition 0.000 description 1
- 239000011780 sodium chloride Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/447—Polarisation spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/02—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by astronomical means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Astronomy & Astrophysics (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a multi-angle data recombination matching method of L1-grade radiation/polarization observation and observation geometry, which is suitable for a high-resolution five-number satellite multi-angle polarization imager (DPC/GF-5) and similar satellite-borne sensors, and comprises an entire orbit image data blocking technology; a reference pixel selection method; an adjacent pixel observation angle matching method based on combination of an observation zenith angle and an observation azimuth angle; pixel-by-pixel multi-angle data sequencing and recombining technology. The multi-angle recombination matching method of the radiation/polarization observation data can solve the problems of data jumping and banding caused by cutting and splicing of the domestic multi-angle polarization satellite image, overcomes the influence of the data jumping and banding on the inversion of atmospheric and earth surface parameters, and effectively improves the quantitative inversion accuracy.
Description
Technical Field
The invention relates to a satellite image multi-angle data recombination matching method in the fields of remote sensing science and image processing, which is suitable for a multi-angle radiation/polarization data recombination matching method of a multi-angle polarization satellite, in particular to a domestic high-resolution satellite.
Background
The satellite remote sensing earth imaging observation technology is one of the most effective means for realizing rapid detection of a plurality of circle layers of earth land, ocean, atmosphere and the like at present, and has the detection advantages of continuous space coverage, large-range information detection, instantaneous imaging, high spatial resolution and the like, so that the requirements of a plurality of different fields of climate, environment, resources, ecology and the like on quantitative and qualitative detection of key parameters of the earth can be met, and the satellite remote sensing earth imaging observation technology becomes a research hotspot and an important development direction in recent decades. In remote sensing quantification application, the imaging quality and the image preprocessing effect of a remote sensing image are important influencing factors for determining the inversion accuracy of remote sensing parameters, for example, radiation correction accuracy, spectrum correction accuracy, geometric registration accuracy, atmospheric correction effect and the like are important indexes which have non-negligible influence on the remote sensing quantification inversion result.
The atmospheric particulates refer to solid or liquid particle suspended substances in the earth atmosphere, and comprise particles derived from naturally-generated sand dust, sea salt, volcanic ash, biological aerosol and the like, and carbon aerosol, secondary particulates and the like generated by artificial activity discharge, and the particle size range spans up to 5 orders of magnitude, so that the atmospheric particulates become an important component of an earth complex huge system. The content of atmospheric particulates and the characteristics of the atmospheric particulates in the aspects of optics, physics, chemistry, radiation and the like have important influence on the air quality, climate change, earth observation and other national social development levels and scientific research fields, so that the atmospheric particulates are one of key targets of satellite remote sensing detection.
The atmospheric particulate satellite remote sensing detection has been developed for decades and is established and perfected by multiple technologies such as single-channel detection, multi-channel detection, intensity detection, polarization detection, multi-angle detection and the like. A representative atmospheric particulate detection satellite sensor includes: the MODIS sensor on the Aqua satellite is provided with a visible light to short wave infrared detection channel, and a mature aerosol dark target inversion algorithm and a deep blue inversion algorithm are developed; the MISR sensor carried on the Terra satellite can realize the observation of a target pixel under a plurality of angles, and correspondingly develops an aerosol inversion algorithm adaptive to other detection at a plurality of angles; the CALIPO satellite carries an active laser radar CALIPO and obtains the information of the aerosol in the vertical direction by receiving the echo signal; the geostationary satellite detects a specific region of the earth in a staring mode and can obtain a regional aerosol monitoring result in a high time phase, and representative sensors comprise GOCI/COMS in Korea, AHI/HIMAWARI-8 in Japan and PMS/GF-4 in high-grade series in China.
A common limitation of the satellite sensors is that the observation dimensions are relatively small, so that only the optical thickness parameter of the aerosol can be obtained, and the detection capability of other key parameters, such as the physical properties of the atmospheric particulates, such as the absorptivity and the particle size, does not form a mature technical method. The POLDER-3 sensor carried on a PARASOL satellite in France has the multi-angle, multi-band, strength and polarization detection capability, is considered to be the most comprehensive and effective satellite-borne detection mode with the strongest detection capability of the aerosol at present, and is widely concerned by researchers.
China transmits a high-resolution five-number satellite in a high-resolution earth observation system in 5 months in 2018, carries two earth observation sensors, namely a visible light-short wave infrared hyperspectral camera and a full-spectrum spectral imager, and four advanced atmosphere observation loads, namely a multi-angle polarization imager, an atmospheric environment infrared ultrahigh spectral resolution detector, an atmosphere main greenhouse gas monitor and an atmosphere trace gas differential absorption spectrometer, and is an environment monitoring flagship satellite in a high-resolution satellite series in China. The multi-angle Polarization imager (DPC) is a satellite-borne wide-field imager (1850 km in width) with a plurality of spectral bands (covering visible light to near infrared), a plurality of angles (9-12 angle imaging) and Polarization (detecting 3 Polarization components) in China, has a special detection channel for detecting atmospheric composition information such as aerosol, cloud, water vapor and the like, and has the detection capability of both land and marine environments. The DPC realizes the detection of the polarization and intensity radiation information of a target pixel under at most 12 angles by continuous high-speed imaging along the direction of the track, and obtains the polarization and intensity radiation signals on different spectral channels by the rotation and the coupling of the optical filter and the polarization plate wheel. The DPC/GF-5 emitted by China and a French POLDER-3 sensor adopt a similar advanced detection mechanism, have the high-precision detection capability of multiple dimensions of spectrum, angle, intensity and polarization, have the spatial resolution of 3.3 km, which is 1 time higher than the POLDER-3 resolution (6 x 7 km), and can meet the requirements of important applications such as urban scale fine atmospheric detection, regional pollution transmission channel monitoring and the like on the high spatial resolution of atmospheric products.
However, the current data are not ideal when used for the quantitative inversion of atmospheric parameters.
Disclosure of Invention
Research shows that DPC is similar to POLDER-3/PARASOL, and when a standardized data product (L1 level) is produced, radiation and polarization observation data of multiple observation angles are cut, adjusted and spliced according to the earth observation sequence, so that more complete observation information is concentrated in observation angles which are ranked in the front (the observation angles of the data imaged in the same scene are the same), and the continuity and readability of the data are improved. However, a problem is also caused by that a corresponding remote sensing image is not actually a scene image acquired at the same time when the sensor images under a certain angle, but a result obtained after the observation data under a plurality of angles are cut and spliced according to a certain rule. This method causes misalignment at the cut edge in the image, which is mainly represented by the observed quantity with angle change characteristics, such as the corresponding observed zenith angle, the observed azimuth angle, the intensity and the polarized radiation, and the numerical value of the observed quantity generates large jitter. When the spatial domain processing of images is involved, the data organization form is not beneficial to quantitative inversion, and particularly for aerosol parameter inversion with relatively sensitive observation geometry, abnormal bands with discontinuous space may appear in the inversion result. Therefore, the L1-level data products of DPC and similar sensors need to be reprocessed, and single-scene data imaged at the same time during sensor shooting is recovered, so that the precision of quantitative inversion and processing of satellite remote sensing data is improved.
At present, no research or report on a multi-angle data reorganization matching method and technology for the loads is found. The method for recombining and matching the multi-angle remote sensing data, which is oriented to the application requirements of the domestic multi-angle polarized satellite, is urgently needed to be developed, and an important data basis is provided for improving the quantitative inversion accuracy of the atmospheric parameters.
The invention aims to provide a data preprocessing method of a multi-angle polarization satellite for improving the accuracy of quantitative inversion of atmospheric parameters; the specific technical scheme is as follows:
a data preprocessing method for a multi-angle polarized satellite comprises the following steps:
1) reading observation angle number information in the whole-orbit data, judging effective total line number and total column number, and segmenting the acquired whole-orbit satellite data to obtain initial blocks;
2) judging whether at least one complete data column exists in the initial block; if yes, as the data block to be processed, executing the step 4)
3) Dividing the initial block into a plurality of sub-blocks with at least one complete data column; the sub-blocks are used as data blocks to be processed;
4) extracting observation data in the data block to be processed;
5) traversing from one end point in the complete data column as a starting point to the other end point in sequence, wherein the current point is used as a reference pixel and the next point is used as a pixel to be processed to form a reference pixel-pixel to be processed pair; all reference pixel-pixel pairs to be processed form a reference column adjacent pixel pair data set;
6) Each point in the complete data column is taken as a starting point, traversal is sequentially carried out leftwards, the current point is taken as a reference pixel, the next point is taken as a pixel to be processed, and a reference pixel-pixel to be processed pair is formed; all reference pixel-to-be-processed pixel pairs form a left adjacent pixel pair data set; traversing sequentially to the right, and forming a right adjacent pixel pair data set by adopting the same method;
7) calculating the data of all observation angles of the reference pixel and the data of the current observation angle of the pixel to be processed by adopting a formula (1) and a formula (2), and judging whether each formed reference pixel-pixel to be processed pair is matched;
δz=VZAref,i-VZAtest （1）；
δa=VAAref,i-VAAtest （2）；
wherein, delta z is the difference value of the observation zenith angle, i is the serial number of the observation angle, VZAref,iThe ith observation zenith angle, VZA, of the reference pixeltestThe observation zenith angle of the current observation angle of the pixel to be processed is delta a, the difference value of the observation azimuth angle is VAAref,iIs the i-th observation azimuth, VAA, of the reference pixeltestThe observation azimuth angle of the current observation angle of the pixel to be processed is obtained;
δ z is smaller than the observation zenith angle threshold Tz and δ a is smaller than the observation azimuth angle Ta; judging the matching and entering the step 9);
otherwise, judging as mismatching; entering step 8)
8) Selecting data of a next observation angle of the pixel to be processed and the reference pixel, and matching the data and the reference pixel in the step 7) until the data and the reference pixel are matched;
9) The pixel to be processed is used as a reference pixel, and the matched observation angle is used as an initial observation angle of the reference pixel;
10) processing the reference column adjacent pixel pair data set, the left adjacent pixel pair data set and the right adjacent pixel pair data set by adopting the same method in the step 7) until all reference pixel-pixel pairs to be processed in the data sets are matched;
11) and replacing the observation data corresponding to each observation angle of all the reference pixels subjected to matching processing with the original data to finish multi-angle sequencing recombination.
Further, the Tz ranges from 0.5 to 1.0 °; ta ranges from 1.0 to 2.0 deg..
Further, the observation data includes geographic coordinate domain information and data domain information of each wave band and each angle.
Further, the geographic coordinate domain information comprises surface elevation, sea-land mask, longitude and latitude, and grid row and column numbers.
Further, the data domain information comprises radiance, polarization Stokes components and observation geometry.
Further, the method also comprises the following steps:
12) calculating the moving sequence step length of the observation angle according to the initial observation angle serial number AnS and the effective observation angle number N of the pixel to be processed:
the data of the initial observation angle serial number AnS and the future data are transposed with the data before the initial observation angle serial number AnS, the data are recombined, the overall difference value of VZA and VAA under each observation angle of the reference pixel and the pixel to be processed is calculated, and whether all the observation angles meet the matching condition is judged:
Wherein i represents observation angle serial numbers (N in total), subscripts ref and test respectively represent a reference pixel and a pixel to be processed, Tz is an observation zenith angle threshold value and Ta is an observation azimuth angle threshold value;
if yes, the data check is successful; if not, processing from step 1) to step 12) is carried out on the new data; and finding and eliminating the observation angle of the abnormal data.
The satellite data is processed by the steps of performing data blocking of standard line number on the data, forming a reference pixel and a pixel pair to be processed, sequencing, recombining, adjusting and the like one by one; the multi-angle data recombination matching method is effective and stable, and the processing result can meet the data base requirement of the atmospheric parameter quantitative inversion.
Drawings
FIG. 1 is a flow chart of a multi-angle data reorganization and matching method proposed by the present invention;
FIG. 2 is a schematic diagram of an embodiment of a process for further data blocking according to reference pixel selection;
FIG. 3 is an L1 level data image of DPC/GF-5 processed by standard line number data blocking (670 nm band, Stokes vector I, Q, U of the first observation angle, and observation zenith angle, observation azimuth) in an embodiment;
FIG. 4 is the image data after matching by multi-angle data reconstruction in the embodiment (670 nm band, Stokes vector I, Q, U of the first observation angle, and observation zenith angle and observation azimuth angle).
Detailed Description
The present invention will now be more fully described with reference to the following examples. This invention may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein.
The specific implementation mode of the invention is described by taking the actual observation data of the domestic multi-angle polarized satellite sensor DPC/GF-5 as an example. The embodiment data adopted by the invention is DPC/GF-5 observation data (data time: 2019, 5 months and 5 days, data space range: east China area); the method according to the invention uses a computer to process the data. The technical flow chart of the method is shown in figure 1.
(1) Whole track image data chunking
Firstly, reading the observation angle number information in the whole track data, and judging the effective total row number and the total column number of the track data through an invalid value or a filling value. Secondly, setting a standard block line number (which can be set according to the processing performance of a computer), and carrying out block processing on the whole track data by band and angle according to the set value. DPC/GF-5 embodiment data partial example results of data blocking with standard row number (500 rows) are shown in FIG. 3, including 670 nm band polarization Stokes vectors I670, Q670, U670, observation zenith angle VZA, observation azimuth angle VAA.
The conventional data blocking process can complete the above steps, but for the target of multi-angle data reconstruction which needs to be finally realized by the present invention, the blocked image may also face various adverse situations, especially the image in a high latitude area (close to north and south) is prone to have irregular shapes, for example, the image has sharp corners and edges, the image aspect ratio is too large, so that multiple columns of invalid data appear at the edges, and further targeted processing is needed. On the basis of standard line partitioning, the invention has the innovation that the partitioning processing is further carried out according to the requirement of subsequent reference pixel selection, and the invalid result of the reference pixel in the data reorganization process is avoided. And judging whether each data block after the preliminary blocking meets the requirement of reference pixel selection, namely, the blocked image has at least one complete data column, and all pixels in the data column are effective data. Note that in the DPC/GF-5 embodiment, no complete data column exists in the block data, so that the block processing needs to be continued on the data block, and according to the actual situation of the data block, the data block is further partitioned by rows by using a bisection method, which satisfies that at least one complete data column exists in each of the upper and lower sub-blocks. DPC/GF-5 embodiment the process schematic of data further data blocking is shown in FIG. 2. Of course, three or more divisions by row may be made as necessary. After segmentation, the reference column is as close as possible to the middle of all the complete columns.
And finally, extracting corresponding observation data according to the blocking result, wherein the observation data comprises data domain information such as the radiation brightness of each wave band and each angle, polarization Stokes components, observation geometry and the like, and geographic coordinate domain information such as earth surface elevation, sea-land masks, longitude and latitude, grid row numbers and column numbers and the like. And finally, writing the extracted block data information into a new block data file according to the original L1-level whole track data file storage format to finish data blocking processing.
(2) Block data reference pixel selection
The core idea of multi-angle data reorganization and matching provided by the invention is to establish a one-to-one corresponding reference pixel-to-be-processed pixel pair for each pixel in a block image, and adjust the multi-angle sequencing of the to-be-processed pixels according to the reference pixels. Except for the image edge, each pixel is a reference pixel and a pixel to be processed, the multi-angle sequence of a certain pixel is determined by the reference pixel, and the multi-angle sequence of the next pixel to be processed is also determined. Therefore, the correct selection of the reference pels of the block data is an important prerequisite for the implementation of the invention. The core problem to be solved by this step is how to determine its reference pixel for each pixel in the image.
There are generally two conventional methods for performing pixel-by-pixel traversal on a remote sensing image. One method is to traverse along the longitudinal direction and the transverse direction from the corner point, but due to the special imaging mode and the data projection characteristics of the load such as DPC/GF-5, the segmented image data is not a standard rectangle or square but an approximate parallelogram or trapezoidal quadrilateral, and the method can encounter a large amount of invalid data conditions to cause processing failure. The other method is that the pixel is randomly selected from the middle of the image to start traversing in four directions of up, down, left and right, and the pixel is judged to reach the edge of the image and stop when an invalid value is metThe subsequent multi-angle reorganization processing is more complex and error-prone, and the amount of calculation is larger. In order to solve the problem, the invention innovatively provides a reference pixel traversing method based on double cycles of 'points' (reference pixels) 'columns' (reference data columns) 'rows' (each row). First, a starting reference pel seed is determined according to the block data. Since the blocking strategy that at least one complete data column exists needs to be satisfied in the blocking step, the initial reference pixel is selected from all columns { C ] satisfying the requirement iSelect from among { C }, typicallyiCentral column of (C)m) The bottommost row (R)b) As the starting reference pixel seed (line number R)bColumn number is Cm). Then, traversing the adjacent pixels from seed to pixel upwards to construct CmReference pixel-to-be-processed pixel pair of pixels of each column and row, i.e. (R)b, Cm)ßà(Rb-1, Cm)、(Rb-1, Cm)ßà(Rb-2, Cm) …, and so on. Then, refer to the data column CmEach row of pixels is taken as a reference, and the pixels are traversed pixel by pixel left and right respectively to construct a corresponding reference pixel-pixel pair to be processed, namely (R)b, Cm)ßà(Rb, Cm-1) and (R)b, Cm)ßà(Rb, Cm+1)、(Rb-1, Cm)ßà(Rb-1, Cm-1) and (R)b-1, Cm)ßà(Rb-1, Cm+1), …, and so on. And finally, establishing the corresponding relation between the reference pixels and the pixels to be processed of all the pixels in the data block to form a data set of adjacent pixel pairs.
(3) Adjacent pixel observation angle matching
After an adjacent pixel pair data set is formed, observation angle matching is carried out on each group of reference pixels and pixels to be processed, and the purpose is to find the initial observation angle serial number (consistent with the reference pixels) of the pixels to be processed, so that an initial value is provided for subsequent multi-angle data sequencing and recombining processing. When the DPC/GF-5 sensor images under the same observation angle, the change of the observed zenith angle VZA is small, and after the cutting and splicing, obvious jump can occur at the spliced edge (imaging under the non-same observation angle), so that whether the imaging is under the same observation angle can be judged by setting a jump threshold of the observed zenith angle. However, each pixel of DPC/GF-5 data has 9-12 observation angles, VZA basically presents a forward-looking and backward-looking symmetrical change distribution taking a nadir direction VZA as a minimum value according to a change sequence from large to small to large, and correct matching is difficult to carry out only depending on the numerical value of an observation zenith angle.
The invention provides a new technical idea to solve the problems, and an observation azimuth angle is added for auxiliary judgment on the basis of judgment and matching of observation zenith angles. Firstly, setting jump thresholds Tz and Ta of VZA and VAA according to numerical change rules of observed zenith angles VZA and observed azimuth angles VAA in block data, namely when VZA difference between a reference pixel and a pixel to be processed is smaller than Tz and VAA difference is smaller than Ta, judging that the pixel to be processed and the reference pixel are imaged at the same observation angle, and cutting and splicing do not occur. Then, matching the VZA and the VAA of the reference pixel at other observation angles with the VZA and the VAA of the pixel to be processed at the first observation angle (namely, no jump occurs), and performing the following processing according to the matching condition: if only one matching result exists in j, the matching angle is the initial observation angle AnS of the pixel to be processed; k if more than one matching result exists, selecting a matching angle with overall smaller deltaz and deltaa as the initial observation angle AnS of the pixel to be processed; if the matching result does not exist, the fact that the first observation angle of the pixel to be processed is not the same as the first observation angle of the pixel to be processed is shown, the observation angle of the pixel to be processed is changed, the steps are repeated, and the judgment is continued, whether VZA and VAA under each observation angle of the reference pixel are matched with VZA and VAA under the second observation angle of the pixel to be processed exists or not is judged, and therefore the initial observation angle of the pixel to be processed is searched. And after the matching is successful, the data of the pixel to be processed replaces the data of the reference pixel in the next reference pixel-pixel pair to be processed, and the matching is carried out. When matching, matching all reference pixel-pixel pairs to be processed in the data set of the adjacent pixel pairs of the reference column; and respectively processing all reference pixel-to-be-processed pixel pairs in the left adjacent pixel pair data set and the right adjacent pixel pair data set by taking the processed reference column data as a reference. And finally, establishing an initial observation angle serial number of each pixel to be processed for subsequent pixel-by-pixel sequencing recombination. The jump calculation formula is as follows:
δz=VZAref,i-VZAtest （1）；
δa=VAAref,i-VAAtest （2）；
(4) Pixel-by-pixel multi-angle data sequencing reorganization
After the initial observation angle sequence number of the pixel to be processed is determined, the data under each angle can be recombined in sequence according to the change rule of the observation zenith angle and the observation azimuth angle. However, since some pixels have some missing individual observation angles, performing only sequential reorganization results in sequential misplacement of angles following the missing angle. Therefore, in the actual data processing process, the invention adopts the strategy of successive reorganization and matching detection, namely, matching detection is carried out after each reorganization, the rest observation angles which are not matched are reprocessed, and the observation angles in which abnormal data are positioned are found and eliminated until the matching of all effective observation angles is completed. The method comprises the following specific steps:
firstly, according to the initial observation angle sequence number AnS and the effective observation angle number N of the pixel to be processed, calculating the step length of the observation angle moving sequence:
and moving the AnS th observation angle to the last observation angle of the pixel to be processed to the first L +1 angle of the new data, and sequentially moving the first AnS-1 angle of the pixel to be processed to the L +2 th angle to the last angle of the new data to finish the first recombination. Then, by calculating the overall difference value of VZA and VAA under each observation angle of the reference pixel and the pixel to be processed, whether all the observation angles meet the matching condition is judged:
Wherein i represents observation angle serial numbers (N in total), subscripts ref and test respectively represent a reference pixel and a pixel to be processed, and TzFor observing zenith angle threshold and TaTo observe the azimuth threshold.
If unmatched observation angles still exist, further screening the observation angles which are not matched according to a threshold value, taking the first observation angle as a new initial observation angle serial number, repeating the steps until all the observation angles of the pixel to be processed are matched with the reference pixel, satisfying the formulas (4) and (5), and outputting an observation angle sequence adjustment array of the pixel to be processed. Said Tz is preferably in the range of 0.5-1.0 °; ta ranges from 1.0 to 2.0 deg.. The threshold is too low to guarantee a perfect match; the time consumption of the calculation increases drastically when the threshold is too high.
And finally, rewriting multi-band intensity radiation, a polarized Stokes vector, an observation zenith angle, an observation azimuth angle, a sun zenith angle, a sun azimuth angle and the like of the pixel to be processed at each observation angle into a new data file according to the sequence adjusting array, and finishing multi-angle sequencing recombination. The result of pixel-by-pixel multi-angle sequencing and recombination of block data in the DPC/GF-5 embodiment is shown in figure 4, and it can be seen that the jump of a Stokes vector I, Q, U image caused by cutting and splicing in original L1-level data (figure 3) is corrected, the observation zenith angle and observation azimuth angle strips are restored into images when the sensor is imaged, and the change rule accords with a physical scene, which shows that the multi-angle data recombination matching method provided by the invention is effective and stable and can meet the data base requirement of atmospheric parameter quantitative inversion.
The above examples are only for illustrating the present invention, and besides, there are many different embodiments, which can be conceived by those skilled in the art after understanding the idea of the present invention, and therefore, they are not listed here.
Claims (6)
1. A data preprocessing method for a multi-angle polarized satellite is characterized by comprising the following steps:
1) reading observation angle number information in the whole-orbit data, judging effective total line number and total column number, and segmenting the acquired whole-orbit satellite data to obtain initial blocks;
2) judging whether at least one complete data column exists in the initial block; if yes, as the data block to be processed, executing the step 4)
3) Dividing the initial block into a plurality of sub-blocks with at least one complete data column; the sub-blocks are used as data blocks to be processed;
4) extracting observation data in the data block to be processed;
5) traversing from one end point in the complete data column as a starting point to the other end point in sequence, wherein the current point is used as a reference pixel and the next point is used as a pixel to be processed to form a reference pixel-pixel to be processed pair; all reference pixel-pixel pairs to be processed form a reference column adjacent pixel pair data set;
6) Traversing each point in the complete data column serving as a starting point in sequence leftwards, wherein the current point serves as a reference pixel and the next point serves as a pixel to be processed to form a reference pixel-pixel to be processed pair; all reference pixel-to-be-processed pixel pairs form a left adjacent pixel pair data set; traversing sequentially to the right, and forming a right adjacent pixel pair data set by adopting the same method;
7) calculating the data of all observation angles of the reference pixel and the data of the current observation angle of the pixel to be processed by adopting a formula (1) and a formula (2), and judging whether each formed reference pixel-pixel to be processed pair is matched;
δz=VZAref,i-VZAtest （1）；
δa=VAAref,i-VAAtest （2）；
wherein, delta z is the difference value of the observation zenith angle, i is the serial number of the observation angle, VZAref,iIs the ith observation of the reference pixelZenith angle, VZAtestThe observation zenith angle of the current observation angle of the pixel to be processed is delta a, the difference value of the observation azimuth angle is VAAref,iIs the i-th observation azimuth, VAA, of the reference pixeltestThe observation azimuth angle of the current observation angle of the pixel to be processed is obtained;
δ z is smaller than the observation zenith angle threshold Tz and δ a is smaller than the observation azimuth angle Ta; judging the matching and entering the step 9);
otherwise, judging as mismatching; entering step 8)
8) Selecting data of a next observation angle of the pixel to be processed and the reference pixel, and matching the data and the reference pixel in the step 7) until the data and the reference pixel are matched;
9) The pixel to be processed is used as a reference pixel, and the matched observation angle is used as an initial observation angle of the reference pixel;
10) processing the reference column adjacent pixel pair data set, the left adjacent pixel pair data set and the right adjacent pixel pair data set by adopting the same method in the step 7) until all reference pixel-to-be-processed pixel pairs in the data sets are matched;
11) and replacing the observation data corresponding to each observation angle of all the reference pixels subjected to matching processing with the original data to finish multi-angle sequencing recombination.
2. The method for pre-processing data for a multi-angle polarized satellite of claim 1, wherein Tz ranges from 0.5 to 1.0 °; ta ranges from 1.0 to 2.0 deg.
3. The method for pre-processing data of a multi-angle polarized satellite according to claim 1, wherein the observation data comprises geographical coordinate domain information and data domain information for each wave band and each angle.
4. The method for preprocessing data of a multi-angle polarized satellite of claim 3, wherein the geographic coordinate domain information comprises surface elevation, sea-land mask, latitude and longitude, grid row and column number.
5. The method for data pre-processing of a multi-angle polarized satellite of claim 3, wherein the data domain information comprises radiance, polarized Stokes components, observation geometry.
6. The method for pre-processing data for a multi-angle polarized satellite of claim 1, further comprising the steps of:
12) calculating the step length of the movement sequence of the observation angle according to the initial observation angle sequence number AnS and the effective observation angle number N of the pixel to be processed:
the data of the initial observation angle sequence number AnS and the subsequent data are transposed with the data of the initial observation angle sequence number AnS, the data are recombined, the overall difference value of VZA and VAA under each observation angle of the reference pixel and the pixel to be processed is calculated, and whether all the observation angles meet the matching condition is judged:
wherein i represents an observation angle sequence number; the number of the observation angles is N, subscripts ref and test respectively represent a reference pixel and a pixel to be processed, Tz is an observation zenith angle threshold value and Ta is an observation azimuth angle threshold value;
if yes, the data check is successful; if not, processing the new data from the step 1) to the step 12); and finding and eliminating the observation angle of the abnormal data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110222941.XA CN112945384B (en) | 2021-03-01 | 2021-03-01 | Data preprocessing method for multi-angle polarization satellite |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110222941.XA CN112945384B (en) | 2021-03-01 | 2021-03-01 | Data preprocessing method for multi-angle polarization satellite |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112945384A CN112945384A (en) | 2021-06-11 |
| CN112945384B true CN112945384B (en) | 2022-06-28 |
Family
ID=76246770
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110222941.XA Active CN112945384B (en) | 2021-03-01 | 2021-03-01 | Data preprocessing method for multi-angle polarization satellite |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112945384B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114235713B (en) * | 2021-12-20 | 2024-05-31 | 中国科学院合肥物质科学研究院 | Method and device for preprocessing satellite-borne polarization scanning remote sensor data |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ATE443846T1 (en) * | 2004-09-15 | 2009-10-15 | Deutsch Zentr Luft & Raumfahrt | PROCESSING OF REMOTE SENSING DATA |
| WO2012011246A1 (en) * | 2010-07-21 | 2012-01-26 | パナソニック株式会社 | Image processing device |
| CN102103204B (en) * | 2011-01-26 | 2013-05-22 | 环境保护部卫星环境应用中心 | Inversion method for land aerosols optical thickness based on environment satellite 1 |
| CN111753439B (en) * | 2020-07-09 | 2023-07-21 | 中国科学院空天信息创新研究院 | Aerosol optical thickness inversion method of multi-angle polarized satellite sensor |
-
2021
- 2021-03-01 CN CN202110222941.XA patent/CN112945384B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112945384A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107063296B (en) | on-orbit radiation calibration method for satellite remote sensing sensor | |
| CN102324098B (en) | Relative radiation calibration method in combination with laboratory calibration and even landscape statistics | |
| CN108564532B (en) | Large-scale ground distance satellite-borne SAR image mosaic method | |
| CN111144350B (en) | Remote sensing image positioning accuracy evaluation method based on reference base map | |
| CN107966210B (en) | Hyperspectral image-based thermal infrared fusion reconstruction method | |
| CN111145351B (en) | Minnarert terrain correction model optimization method considering ground feature types | |
| CN113744249B (en) | Marine ecological environment damage investigation method | |
| CN114022783A (en) | Satellite image-based water and soil conservation ecological function remote sensing monitoring method and device | |
| CN107093196A (en) | The in-orbit relative radiometric calibration method of video satellite area array cameras | |
| CN112945384B (en) | Data preprocessing method for multi-angle polarization satellite | |
| CN114708211B (en) | Optimization processing method and system for satellite remote sensing imaging | |
| CN105139396A (en) | Automatic remote sensing image haze detection method | |
| CN110837090B (en) | Correction method for interference ripple of satellite-borne hyperspectral infrared image | |
| CN111175231B (en) | Inversion method and device of canopy vegetation index and server | |
| Narayanan et al. | Imaging observations of upper mesospheric nightglow emissions from Tirunelveli (8.7 o N) | |
| CN113643244B (en) | Quick detection method for flare of optical remote sensing satellite image water body | |
| CN113936009B (en) | Cloud shadow removing method, device and equipment for meteorological satellite flood monitoring | |
| Zhang et al. | Fusion of ascending and descending polarimetric SAR data for color orthophoto generation | |
| Liu et al. | Vignetting and chromatic aberration correction for multiple spaceborne CCDS | |
| Chakhvashvili et al. | Lai and leaf chlorophyll content retrieval under changing spatial scale using a uav-mounted multispectral camera | |
| Kozelov et al. | A study of rayed structures in auroras by triangulation methods: 1. Height profiles of volume emission rate | |
| Crespi et al. | Radiometric quality and DSM generation analysis of CartoSat-1 stereo imagery | |
| CN115205682B (en) | NDVI maximum value remote sensing data product seamless production processing method | |
| CN114234934B (en) | Ocean internal wave monitoring method and system of non-uniform spatial resolution satellite-borne low-light-level imager | |
| CN114119769B (en) | High-precision yaw relative radiation calibration method based on uniform field |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |