CN112465846B - Cloud-containing remote sensing image compression method based on filling strategy - Google Patents
Cloud-containing remote sensing image compression method based on filling strategy Download PDFInfo
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Abstract
The invention discloses a cloud-containing remote sensing image compression method based on a filling strategy, and mainly solves the problems that in the prior art, more high-frequency information still exists at the edge of a cloud area after filling, and coding consumption is high. The scheme is as follows: carrying out boundary expansion on the input cloud-containing image and the cloud mask image; calculating an approximate median of a region of interest in the cloud-containing image; acquiring a gray mask for marking a cloud area and a gray image for marking the edge of the cloud area; calculating the pixel mean value in the edge of each cloud area and filling the corresponding cloud area; carrying out edge mean filtering on the filled image; carrying out level shift on the filtered image by using the approximate median of the region of interest to obtain a preprocessed image; coding the preprocessed image to obtain a compressed code stream and sending the compressed code stream to a receiving end; and the receiving end decodes the received code stream to obtain a recovered image. The method can effectively reduce the coding rate of the cloud region, improve the recovery quality of the region of interest, and can be used for transmitting various cloud-containing images.
Description
Technical Field
The invention belongs to the technical field of image processing, and further relates to a method for efficiently compressing a remote sensing image containing an invalid cloud area, which can be used for transmitting and processing various remote sensing images containing clouds.
Background
The rapid development of high-resolution remote sensing technology makes the contradiction between satellite image transmission bandwidth and remote sensing image data volume continuously aggravated, and a rapid and efficient data compression algorithm is needed for transmitting and processing the acquired image. However, cloud regions which exist universally in the remote sensing images occupy a large amount of storage space and transmission bandwidth, and the bandwidth utilization rate of data ROI of the region of interest, namely terrain region data except the cloud regions in the images is reduced. Therefore, it is of great significance to research image compression algorithms containing invalid cloud region data. In many documents, several compression algorithms for images containing invalid regions have been proposed, which are mainly classified into two categories:
one class of methods is to modify the interior of the existing compression framework to achieve preferential coding of regions of interest or to skip over invalid data regions, which includes the MaxShift method, the Scaling-based method, the shape adaptive wavelet transform SA-DWT method, and the shape adaptive bit-plane coding SA-BPE method. The MaxShift method realizes the preferential coding of the interested area by improving the interested area coefficient or inhibiting the invalid area coefficient; the Scaling-based method realizes the preferential coding of the interested region by setting different priorities for the interested region; the shape adaptive wavelet transform SA-DWT method is improved by applying a mirror effect on the boundary of a data area; the shape adaptive bit-plane coding SA-BPE method avoids coding coefficients belonging to invalid data regions by skipping the coefficients in a fractional bit-plane coder at the Tier-1 coding level.
Another method is to pre-process the image as a front end of the existing compression framework to reduce the coding consumption of the invalid region as much as possible, which includes the Phagecyte algorithm, the ADR algorithm, and the LEC algorithm. The Phagecyte algorithm fills the invalid region by using the arithmetic mean value of the pixels in the window of the specified region to adapt to local image change, which is applied in the commercial GIS; the ADR algorithm fills the invalid region by adopting the average value of the interested region; the LEC algorithm fills the invalid region through the data average value in the local edge context, reduces the coding rate of the invalid region and simultaneously reduces the high-frequency information of the edge part.
Although the first method achieves the purpose of compressing invalid data areas by modifying the interior of the existing compression frame, the method is only suitable for processing images with interested areas in regular shapes, and for cloud-containing remote sensing images, the pixel blocks of the interested areas in the mapping process of each sub-band can be regarded as cloud pixel blocks due to the irregularity of the cloud areas, the coefficients of the interested areas are reduced while the coefficients of the cloud areas are restrained, and the compression performance is influenced.
Compared with the former method, the second method preprocesses the image before the existing compression frame, is easy to realize, has strong portability, and is insensitive to the shape of the region of interest. However, the current pretreatment methods still have defects, among them:
the invalid area filled by the Phagecyte algorithm is not a single gray value, and the coding consumption of the invalid area is still high; the filling value adopted by the ADR algorithm does not consider the edge information of the filling area, so that after wavelet transformation, the edge part can generate more high-frequency information which can generate higher coding consumption;
the LEC algorithm only considers the outer edge information, if the cloud detection algorithm does not detect the cloud of the edge, the pixel value of the filled cloud area is closer to the pixel value of the ground object area, and a larger pixel difference still exists between the filled cloud area and the cloud of the edge, so that higher coding consumption still exists at the edge.
Disclosure of Invention
The invention aims to provide a cloud-containing remote sensing image compression method based on a filling strategy aiming at the defects of the prior art, so that the cloud region coding consumption is reduced to the maximum extent and the compression performance of an interested region is improved by completely eliminating invalid cloud region data.
In order to achieve the purpose, the technical scheme of the invention comprises the following implementation steps:
1) Expanding the boundary containing the cloud remote sensing image and the cloud mask image:
inputting a cloud-containing remote sensing image I and a corresponding cloud mask image M, wherein I is a gray image; m is a binary image, the region of interest is represented by 0, the cloud region is represented by 1, and the two images have the same size;
respectively complementing the width W and the height H of the cloud-containing remote sensing image I and the cloud mask image M into even numbers to obtain a cloud-containing remote sensing image I with an expanded boundary b And expanded cloud mask image M b ；
2) Calculating an approximate median of the region of interest:
respectively expanding the cloud-containing remote sensing image I b And expanded cloud mask image M b The method comprises the steps of spatially dividing a cloud mask image into a series of small square blocks with fixed sizes, and regarding blocks with 0 pixel points in the blocks of the cloud mask image as region-of-interest blocks;
cloud-containing remote sensing image I after calculation expansion b Obtaining a mean value sequence A by taking the mean value of the pixel points in each interested region block, and taking the median of the sequence A as I b An approximate median Z of the region of interest;
3) Acquiring marked cloud area edges:
3a) For the expanded cloud mask image M b Sequentially carrying out expansion and corrosion operations to obtain an expanded cloud mask M d And post-etch cloud mask mark M e ；
3b) For the expanded cloud mask M d The connected region marking is carried out on the cloud region in the image, namely, each connected cloud region is represented by different numbers to obtain a gray image N d (ii) a The expanded cloud mask M d And a post-etching cloud mask M e Performing point-by-point subtraction to obtain a binary image M with 1 representing cloud region edge and 0 representing other regions s ；
3c) Masking the binary cloud with a mask M b And a gray scale image N d Multiplying point by point to obtain a gray mask N for marking different connected cloud areas b ；
3d) The binary image M s And a gray scale image N d Multiplying point by point to obtain a gray image N marking the edges of different cloud regions s ；
4) According to N s Respectively calculating and expanding cloud-containing remote sensing image I of the cloud region edge mark information in b Storing the result as a dictionary D according to the mean value of the pixels in the cloud region b The cloud region mark information and the dictionary D in the Chinese cloud region are used for carrying out the cloud-containing remote sensing image I after the expansion b Filling each kind of cloud area in the image to obtain a filled image I f ；
5) Traversing filled image I f If a certain pixel point I is a pixel point in the interested area f The four neighborhoods of (x, y) are internally provided with pixel points I belonging to cloud areas f (x ', y'), then I f The value at the (x ', y') position is replaced by I f (x ', y') and I f The mean of the pixel values at (x, y);
6) Filling the image I f Subtracting an offset from the values of all the pixels, wherein the value of the offset is equal to the median Z calculated in the step 2, and obtaining a preprocessed image I p ；
7) For the preprocessed image I p Carrying out compression coding to obtain a compressed code stream S and sending the compressed code stream S to a receiving end;
8) The receiving end decodes the received code stream S to obtain a recovery image I r 。
Compared with the prior art, the invention has the following beneficial effects:
first, the cloud area is filled according to the local inner and outer dual-edge context information, so that the problem that more high-frequency information still exists at the edge of the cloud area after filling in the prior art is solved, and the coding consumption is effectively reduced.
Secondly, the invention carries out mean filtering on the edges of the cloud region of the filled image, can further smooth the boundary and fully reduce the high-frequency wavelet coefficient at the edge.
Simulation results show that the cloud region coding rate can be reduced to the maximum extent, and the interesting region recovery quality is improved. Compared with a standard JPEG2000 algorithm, the peak signal-to-noise ratio PSNR index of the region of interest is obviously improved, and the higher the cloud content of the image is, the larger the index improvement is during the same-time compression.
Drawings
FIG. 1 is a general flow chart of an implementation of the present invention;
FIG. 2 is a sub-flowchart of the present invention for obtaining marked cloud region edges;
FIG. 3 is a schematic illustration of filling a cloud region in accordance with the present invention;
fig. 4 is a comparison graph of smoothing effect of the present invention and the prior art method on the cloud region boundary.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
referring to fig. 1, the specific implementation steps of the present invention are as follows:
step 1: and expanding the boundary of the cloud-containing remote sensing image and the cloud mask image.
1.1 Input a cloud-containing remote sensing image I and a corresponding cloud mask image M, wherein I is a gray level image and bit depth is B N (ii) a M is a binary image, the region of interest is represented by 0, the cloud region is represented by 1, and the two images have the same size;
1.2 Carrying out boundary extension on the cloud-containing remote sensing image:
if W is an odd number and H is an even number, adding a row of data to the rightmost side of the original cloud-containing remote sensing image, wherein the added data is the W-th row of data of the original cloud-containing remote sensing image;
if W is an even number and H is an odd number, adding a line of data at the lowest part of the original cloud-containing remote sensing image, wherein the added data is the H-th data of the original cloud-containing remote sensing image;
if W is an odd number and H is an odd number, firstly adding a column of data on the rightmost side of the original cloud-containing remote sensing image, wherein the added data is the W-th column of data of the original cloud-containing remote sensing image, recording the image filled with the column as a picture P, and then adding a row of data on the bottommost side of the picture P, wherein the added data is the H-th row of data of the picture P;
if W is an even number and H is an even number, the original cloud picture is not operated;
marking the cloud picture after the boundary expansion as I b ；
1.3 Boundary extension on the cloud mask image:
the boundary extension process of the cloud mask image is completely consistent with the boundary extension process of the cloud-containing remote sensing image, and the cloud mask image with the extended boundary is marked as M b ；
1.4 ) cloud-containing remote sensing image I after boundary expansion b And expanded cloud mask image M b The widths are W '= W + (W% 2) and the heights are H' = H + (H% 2), where% represents the modulo operation.
Step 2: an approximate median of the region of interest is calculated.
2.1 Combined extended cloud mask M b For extended cloud picture I b Partitioning:
respectively expanding the cloud-containing remote sensing image I b And expanded cloud mask M b The space is divided into a series of small square blocks with the same size, and the side length of each small square block can be selected from three numbers of 8, 4 and 2;
if in fig. I b The width and the height of the square can be completely divided by 8, and the side length of the small square block is set to be 8;
if in fig. I b The width and the height of the square can not be divided by 8 but can be divided by 4, and the side length of the small square block is set to be 4;
if in FIG. I b Width and height of (1) can not be divided by 4 but can be divided by 2, then squareThe side length of the small block is set to be 2;
drawing M b The blocks with the pixel values of all 0 in the blocks are regarded as region-of-interest blocks, and the total number of the region-of-interest blocks is recorded as m;
2.2 ) calculation chart I b Obtaining a mean value sequence A by averaging the pixel points in each interested area block, wherein A = { a = { (a) } 1 ,a 2 ,…,a i ,…,a m In which a is i Means representing the ith region of interest block, i ∈ [1,m ]]；
2.3 Calculate the approximate median of sequence A as I b Approximate median of the region of interest:
opening up an array B = { B = 0 ,b 1 ,…,b j ,…,b 127 Length 128, initialized to 0;
traversing the sequence A to connect the element a i Is divided by the value of
The result of (c) is noted as w, and the element B in the array B is recorded as w Adding 1;
and sequentially accumulating the elements in the array B according to subscripts, and recording the result as:
judging the result of each accumulation, and if the Tth accumulated value is Sum (T) If m/2 is less, continue to accumulate and solve Sum (T+1) ；
If Sum (T) If the sum is more than or equal to m/2, the accumulation is ended and
the value of (d) is taken as the approximate median of the region of interest and is denoted as Z.
And step 3: and acquiring the marked cloud area edge.
Referring to fig. 2, the specific implementation of this step is as follows:
3.1 To the extended cloud mask image M b Sequentially performing expansion and corrosion operations according to 3 multiplied by 3 structural elements to obtain an expanded cloud maskMold M d And post-etch cloud mask M e ；
3.2 Pair of expanded cloud masks M d Marking the connected domain of the cloud region in the middle, and marking M d The cloud region pixels conforming to 8-neighborhood connection are represented by the same reference number to obtain a gray image N d The pixel value range of the gray level image is {0,1,2, \8230;, n }, wherein 0 marks an interested region, l marks the l-th connected cloud region, and n represents the total number of the connected cloud regions;
3.3 Will expand the cloud mask M d With the etched cloud mask M e Performing point-by-point subtraction to obtain a binary cloud edge image M s Wherein 1 represents a cloud area edge and 0 represents other areas;
3.4 Expanded cloud mask image M) b And a grayscale image N d Multiplying point by point to obtain a gray mask N for marking different connected cloud areas b ，N b A set omega of pixel points with n non-zero gray values, wherein the gray value is k k Form the k-th cloud region, omega k ＝{N b (i,j)|N b (i,j)＝k}，k∈[1,n]N is the number of the cloud area;
3.5 Binary cloud edge image M) s And a grayscale image N d Multiplying point by point to obtain a gray image N marking the edges of different cloud regions s ，N s Set Γ of pixels points with n non-zero grey values, where the grey value is k k Constituting class k cloud edges, Γ k ＝{N s (i,j)|N s (i,j)＝k}，k∈[1,n]And n is the number of the cloud area types.
And 4, step 4: and (5) filling the cloud area.
4.1 According to the gray-scale image N s Respectively calculating the cloud picture I after the boundary expansion b The mean values of the pixels in the corresponding area, and the result of these mean values is stored as a dictionary D = { D = { D 1 ,D 2 ,…,D h ,…,D n In which D is h The mean value of the edge pixels of the h-th cloud area is represented, and h belongs to [1, n ]]；
4.2 According to the diagram N) b The region marking information and dictionary D in (1), the graph I b The h-type cloud area in (1) is filled with D h And the filled image is marked as I f As shown in fig. 3.
And 5: and filtering the edge mean value.
According to cloud mask M b Traversing the filled image I f For all interested area pixel points, for each interested area pixel point I f (x, y), judging whether cloud area pixel points exist in four neighborhoods of the image, namely the upper neighborhood, the lower neighborhood, the left neighborhood and the right neighborhood, if the four neighborhoods contain the cloud area pixel points I f (x ', y'), then the cloud area pixel point I f (x ', y') the pixel value is replaced by I f (x ', y') and I f The mean of the pixel values at (x, y).
Step 6: the level is shifted.
Before an image is sent to a compression core, direct current components in an image signal need to be eliminated as much as possible, so that after image transformation, the probability of positive and negative values of wavelet coefficients is basically the same, and the coding efficiency of a compression algorithm is improved.
Aiming at the characteristics of the cloud-containing remote sensing image, the embodiment takes the median of the region of interest as the offset of level shift, namely the approximate median Z of the region of interest obtained by calculation in the step 2 as the offset, and takes the filled image I f Subtracting Z from the values of all the pixel points to complete the level shift, and obtaining a preprocessed image I p 。
And 7: and (5) compression coding.
The preprocessed image I can be processed by, but not limited to, JPEG2000 or JPEG-LS algorithms p Performing compression coding, wherein the example adopts a JPEG2000 algorithm to perform coding, and the coding comprises component transformation, fragment processing, wavelet transformation, quantization, entropy coding, code rate control and code stream organization processes in sequence; if the JPEG-LS algorithm is adopted for compression coding, the method sequentially comprises the processes of context modeling, prediction, residual calculation, parameter updating and Golomb-Rice coding. And coding to obtain a code stream S and sending the code stream S to a receiving end.
And 8: and (5) image decoding.
Using and transmitting terminalsDecoding algorithm matched with the codes is used for decoding the received code stream S, JPEG2000 is adopted for decoding in the example, and the code stream S is subjected to code stream decomposition, entropy decoding, inverse quantization, inverse wavelet transformation and post-processing in sequence to obtain a restored image I r 。
The effects of the present invention can be further illustrated by the following simulations:
1. simulation conditions are as follows:
the remote sensing image shot by the China high score number 1 (GF-1) optical satellite is used for testing.
The image size is 1024 × 1024 with an accuracy of 10 bits.
The number of the test images was 150 in total, and the test images were divided into 3 groups of 50 images each based on the average cloud content.
The existing methods used for simulation include the following three methods:
firstly, a standard JPEG2000 algorithm is adopted;
second, ADR + JPEG2000 method for compressing by JPEG2000 algorithm after preprocessing test image by ADR algorithm
Thirdly, the LEC + JPEG2000 method is adopted to pre-process the test image by the LEC algorithm and then compress by the JPEG2000 algorithm
2. Simulation content:
table 1 test results on average cloud content 30% images
Simulation 2, performing compression test on the test image with average cloud content of 50% by using the invention and the above 3 existing methods, and counting the average value of peak signal-to-noise ratios (PSNR) of the interested areas of the methods under different compression multiples, wherein the result is shown in Table 2, and the unit is dB:
table 2 test results for average cloud 50% image
Simulation 3, performing compression test on the test image with the average cloud content of 70% by using the invention and the above 3 existing methods, and counting the average value of peak signal-to-noise ratios (PSNR) of the interested areas of the methods under different compression multiples, wherein the result is shown in Table 3, and the unit is dB:
table 3 test results for average cloud content 70% image
Simulation 4, performing compression test on a certain test image by using the method of the present invention and the aforementioned ADR + JPEG2000 method and LEC + JPEG2000 method, and comparing the smoothness of cloud region boundaries by the methods, wherein fig. 4 (a) is a cloud-containing remote sensing image, fig. 4 (b) is a cloud mask image, fig. 4 (c) is a partial enlarged view of a cloud region boundary in the figure after the ADR + JPEG2000 method is adopted, fig. 4 (d) is a partial enlarged view of a cloud region boundary in the figure after the LEC + JPEG2000 method is adopted, and fig. 4 (e) is a partial enlarged view of a cloud region boundary in the figure after the ADR + JPEG2000 method is adopted.
3. Simulation conclusion
The data in table 1, table 2 and table 3 all show that the PSNR index of the region of interest of the present invention is greatly higher than that of other methods under the same compression multiple; and the higher the cloud content of the image, the better the compression performance of the invention.
As can be seen from fig. 4, the smoothing effect at the cloud region boundary is significantly better than that of other methods after the method is adopted.
The above description is only one specific example of the present invention and should not be construed as limiting the invention in any way. It will be apparent to persons skilled in the relevant art that various modifications and changes in form and detail can be made therein without departing from the principles and arrangements of the invention, but these modifications and changes are still within the scope of the invention as defined in the appended claims.
Claims (10)
1. A cloud-containing remote sensing image compression method based on a filling strategy is characterized by comprising the following steps:
1) Expanding the boundary of the cloud-containing remote sensing image:
inputting a cloud-containing remote sensing image I and a corresponding cloud mask image M, wherein I is a gray image; m is a binary image, the region of interest is represented by 0, the cloud region is represented by 1, and the two images have the same size;
respectively complementing the width W and the height H of the cloud-containing remote sensing image I and the cloud mask image M into even numbers to obtain a cloud-containing remote sensing image I with an expanded boundary b And expanded cloud mask image M b ；
2) Calculating the approximate median of the region of interest:
respectively expanding the cloud-containing remote sensing image I b And expanded cloud mask image M b The method comprises the steps of spatially dividing a cloud mask image into a series of small square blocks with the same size, and regarding blocks with 0 pixel points in the blocks of the cloud mask image as region-of-interest blocks;
cloud-containing remote sensing image I after calculation expansion b Obtaining a mean value sequence A by taking the median of the pixel points in each interested area block as I b An approximate median Z of the region of interest;
3) Acquiring marked cloud area edges:
3a) For the expanded cloud mask image M b Sequentially carrying out expansion and corrosion operations to obtain an expanded cloud mask M d And post-etch cloud mask mark M e ；
3b) For the expanded cloud mask M d The connected region marking is carried out on the cloud region, namely, each connected cloud region is represented by different numbers to obtain a gray image N d (ii) a The expanded cloud mask M d And a post-etch cloud mask M e Performing point-by-point subtraction to obtain a binary image M with 1 representing cloud region edge and 0 representing other regions s ；
3c) Masking the binary cloud with a mask M b And a gray scale image N d Multiplying point by point to obtain a gray mask N for marking different connected cloud areas b ；
3d) The binary image M s And a gray scale image N d Multiplying point by point to obtain a gray image N marking the edges of different cloud regions s ；
4) According to N s Respectively calculating and expanding cloud-containing remote sensing image I of the cloud region edge mark information in b Storing the result as a dictionary D according to the mean value of the pixels in the cloud region, and calculating the mean value of the pixels in the cloud region according to the gray mask N b The cloud region mark information and the dictionary D in the Chinese cloud region are used for carrying out the cloud-containing remote sensing image I after the expansion b Filling each type of cloud area in the image to obtain a filled image I f ；
5) Traversing filled image I f If a certain pixel point I is a pixel point in the interested area f The four neighborhoods of (x, y) are internally provided with pixel points I belonging to cloud areas f (x ', y'), then I f The value at the (x ', y') position is replaced by I f (x ', y') and I f The mean of the pixel values at (x, y);
6) Filling the image I f Subtracting an offset from the values of all the pixels, wherein the value of the offset is equal to the median Z calculated in the step 2, and obtaining a preprocessed image I p ；
7) For the preprocessed image I p Carrying out compression coding to obtain a compressed code stream S and sending the compressed code stream S to a receiving end;
8) The receiving end decodes the received code stream S to obtain a recovery image I r 。
2. The method according to claim 1, wherein 1) the cloud-containing remote sensing image I after boundary expansion b And expanded cloud mask image M b The width is W' = W + (W% 2), and the height isAre all H' = H + (H% 2), where% represents modulo operation.
3. The method as claimed in claim 1, wherein the side length of the square small block in 2) is selected from three numbers of 8, 4 and 2: if in fig. I b The width and the height of the square can be completely divided by 8, and the side length of the small square block is set to be 8; if in fig. I b The width and the height of the square can not be divided by 8 but can be divided by 4, and the side length of the small square block is set to be 4; if in fig. I b The width and the height of the square can not be divided by 4 but can be divided by 2, and the side length of the square small block is set to be 2.
4. The method of claim 1, wherein the mean sequence A in 2) is represented as follows:
A＝{a 1 ,a 2 ,…,a i ,…,a m }
wherein a is i Means representing the ith region of interest block, i ∈ [1,m ]]And m is the number of region of interest blocks.
5. The method of claim 1, wherein the grayscale cloud mask N in 3 c) b A set omega of n non-zero gray values, where the gray value is k k Form the k-th cloud region, omega k ＝{N b (i,j)|N b (i,j)＝k}，k∈[1,n]And n is the number of the cloud area types.
6. The method according to claim 1, wherein the dictionary D in 4) can be expressed as:
D＝{d 1 ,d 2 ,…,d i ,…,d n }
wherein d is i The mean value of the edge pixels representing the i-th cloud region, i ∈ [1, n ]]When filling, the cloud-containing remote sensing image I after expansion b All the pixel points in the ith cloud area are filled with d i 。
7. The method as claimed in claim 1, wherein the compression coding in 7) adopts JPEG2000 or JPEG-LS compression algorithm.
8. The method of claim 1, wherein 8) the receiving end decodes the received code stream S by using a decoding algorithm matching with the encoding of the transmitting end.
9. The method according to claim 7, characterized in that the compression coding is performed using the JPEG2000 algorithm, which in turn comprises the pre-processing of the image I p The processes of component transformation, fragment processing, wavelet transformation, quantization, entropy coding, code rate control and code stream organization are carried out.
10. Method according to claim 7, characterized in that the compression coding is performed using the JPEG-LS algorithm, which in turn comprises the compression coding of the preprocessed image I p And performing processes of context modeling, prediction, residual calculation, parameter updating and Golomb-Rice coding.
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