CN113298836A - Remote sensing image thin cloud removing method and system considering element contour intensity - Google Patents
Remote sensing image thin cloud removing method and system considering element contour intensity Download PDFInfo
- Publication number
- CN113298836A CN113298836A CN202110727074.5A CN202110727074A CN113298836A CN 113298836 A CN113298836 A CN 113298836A CN 202110727074 A CN202110727074 A CN 202110727074A CN 113298836 A CN113298836 A CN 113298836A
- Authority
- CN
- China
- Prior art keywords
- remote sensing
- sensing image
- element contour
- structural information
- equation
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000000605 extraction Methods 0.000 claims abstract description 20
- 238000001914 filtration Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000003416 augmentation Effects 0.000 claims description 5
- 230000003190 augmentative effect Effects 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 5
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 4
- 238000009795 derivation Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000003321 amplification Effects 0.000 claims description 2
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 2
- 230000009467 reduction Effects 0.000 claims description 2
- 239000000284 extract Substances 0.000 abstract 1
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000005670 electromagnetic radiation Effects 0.000 description 3
- 238000007500 overflow downdraw method Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011426 transformation method Methods 0.000 description 2
- 101100173586 Schizosaccharomyces pombe (strain 972 / ATCC 24843) fft2 gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000003595 mist Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention provides a remote sensing image thin cloud removing method and system considering element contour intensity, which are used for calculating the element contour intensity value of a remote sensing image with thin cloud; constructing a structural information extraction model considering the outline strength of the elements; solving the proposed structural information extraction model to obtain structural information of the remote sensing image; and constructing a thin cloud removal model to obtain a cloud-removed remote sensing image. The method extracts the structural information of the remote sensing image considering the element contour strength, and then removes the thin clouds, so that the boundary and texture information can be well kept while the thin clouds are effectively removed, and additional image support at different time is not needed.
Description
Technical Field
The invention belongs to the field of remote sensing science, and particularly relates to a remote sensing image thin cloud removing method and system considering element contour intensity.
Background
The remote sensing image is an image obtained by acquiring electromagnetic radiation information of an earth surface target through sensors of mobile platforms such as airplanes and satellites and further processing the radiation information by a certain technical means. The remote sensing image contains rich ground object information, can vividly display the position, the category and the state attribute of the ground object, and has wide application in various fields, such as agriculture, military affairs, disaster relief and the like. However, many surface areas are covered by clouds for a long time due to the influence of the external physical environment. As the most common remote sensing image format, the optical remote sensing image cannot effectively penetrate the cloud layer, so that the earth surface image of the area cannot be accurately obtained, the subsequent interpretation work and the application range are seriously influenced, and the usability of the image result is reduced. Thus, cloud layer removal is a valuable research effort in the field of remote sensing images. Cloud layers can be divided into thin clouds and thick clouds in remote sensing images. Although the thin cloud shields the ground object information, a part of electromagnetic radiation information can still be transmitted, and imaging can be partially performed. The thick clouds completely shield the ground feature information and cannot effectively transmit the electromagnetic radiation information, so that only a cloud shape is displayed in the image and the ground feature information of the part cannot be obtained.
The existing thin cloud removing method for the remote sensing image mainly comprises the following three categories: histogram matching method, single image cloud removing method and multi-source data fusion method. The histogram matching method is to match the histogram of one image or one area to another image, so that the image after histogram matching and the original standard image have similar hue and contrast to eliminate cloud and fog. The single remote sensing image cloud removing method only utilizes the information of the image to achieve the aim of removing cloud and fog, and mainly comprises a homomorphic filtering method, a wavelet transformation method and a color transformation method. The multi-source data fusion method is to use the related information of images of different multi-sources and time phases to realize a cloud removing algorithm. The methods can effectively remove the thin cloud in the remote sensing image to a certain extent, but have some problems. The histogram matching method only considers the matching effect of the histogram to achieve the aim of data enhancement, so that cloud and mist are removed. However, the obtained result does not consider the structure and texture information of the image at all, and valuable information such as boundary, texture and the like in the image can be greatly reduced. The single remote sensing image cloud removing method mainly adopts a filtering method, and can seriously blur boundaries and bring some additional artificial phenomena. The multi-source data fusion method needs additional remote sensing images, increases the burden of data acquisition, and the images at different times do not necessarily have accuracy. Therefore, a single remote sensing image cloud removing method is needed, which can well maintain boundary and texture information and does not need additional image support at different time.
Disclosure of Invention
The invention provides a remote sensing image thin cloud removing method and system considering element contour intensity, which can effectively remove thin clouds, well keep boundary and texture information and do not need additional image support at different time.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a remote sensing image thin cloud removing method considering element contour intensity comprises the following steps:
p1: calculating element contour intensity value of remote sensing image with thin cloud
P2: constructing a structural information extraction model considering the outline strength of the elements;
p3: solving the proposed structural information extraction model to obtain structural information of the remote sensing image;
p4: and constructing a thin cloud removal model to obtain a cloud-removed remote sensing image.
Preferably, the method for calculating the element contour intensity value of the remote sensing image with thin clouds in step P1 includes:
p101, calculating corresponding element contour intensity values by using the following formula:
wherein,
forward difference operators in the horizontal direction and the vertical direction respectively;
i, j are pixels of the remote sensing image f, i is 1,2, and M, j is 1, 2.
P102, obtaining the smoothed element contour intensity value by using low-pass filtering on k
Further, the method for constructing the structural information extraction model considering the element contour strength in step P2 includes:
wherein,
and (4) obtaining the smoothed element contour intensity value, wherein u is the structural information of the remote sensing image f, lambda is a data item coefficient, and H is a Gaussian function with the mean value of 1.
Further, the solving process of the structural information extraction model comprises the following steps:
p301, let P ═ u, convert formula (2) as follows:
p302, augmented lagrange form for equation (3):
wherein, zeta is Lagrange multiplier, and r is augmentation coefficient;
p303 and the formula (4) are divided into the following two subproblems to solve:
p304, for equation (5), fixing u, solving for P; the simplification is as follows:
for equation (7) above, there is a closed solution of the form:
p305, for equation (6), fix P and solve u. The reduction for equation (6) is:
a first derivation of the above equation yields the following equation:
the formula (10) is rapidly solved through fast Fourier transform;
p306, updating a Lagrange multiplier;
further, the thin cloud removal model in step P4 is as follows:
wherein I is a remote sensing image after the thin cloud is removed, fdarkAnd the image is a dark channel prior, beta is a positive real number, and u is the structural information of the remote sensing image f.
In another aspect of the present invention, a system for removing thin clouds from remote sensing images considering element contour intensity is further provided, including:
element contour strength calculation module: element contour intensity value for calculating remote sensing image with thin cloud
The structural information extraction model construction module: the system comprises a structural information extraction model for constructing a structural information extraction model considering the outline strength of elements;
a solving module: the remote sensing image extracting module is used for solving the proposed structural information extracting model to obtain structural information of the remote sensing image;
thin cloud removal model construction module: and the method is used for constructing a thin cloud removal model to obtain a cloud-removed remote sensing image.
Preferably, the element contour strength calculation module includes:
a calculation unit: for calculating the corresponding element profile intensity value using the following equation:
wherein,
forward difference operators in the horizontal direction and the vertical direction respectively;
i, j are pixels of the remote sensing image f, i is 1,2, and M, j is 1, 2.
A filtering unit: for obtaining smoothed element contour intensity value by using low-pass filtering on k
Further, the structural information extraction model construction module constructs the model using the following formula:
wherein,
and (4) obtaining the smoothed element contour intensity value, wherein u is the structural information of the remote sensing image f, lambda is a data item coefficient, and H is a Gaussian function with the mean value of 1.
Still further, the solving module includes:
a transformation unit: let p ═ u, convert formula (2) as follows:
an amplification unit: the augmented lagrange form for equation (3) is:
wherein, zeta is Lagrange multiplier, and r is augmentation coefficient;
a subproblem unit: equation (4) is divided into the following two sub-problems to solve:
a first solving unit: for equation (5), fixing u, solving for p; the simplification is as follows:
for equation (7) above, there is a closed solution of the form:
a second solving unit: for equation (6), p is fixed, and u is solved, which for equation (6) is simplified as:
a first derivation of the above equation yields the following equation:
the formula (10) is rapidly solved through fast Fourier transform;
an update unit: updating a Lagrange multiplier;
further, the thin cloud removal model building module builds the thin cloud removal model using the following formula:
wherein I is a remote sensing image after the thin cloud is removed, fdarkAnd the image is a dark channel prior, beta is a positive real number, and u is the structural information of the remote sensing image f.
Compared with the prior art, the invention has the following beneficial effects:
(1) according to the method, the structural information of the remote sensing image considering the element contour strength is extracted, and then the thin cloud is removed, so that the boundary and texture information can be well kept while the thin cloud is effectively removed, and extra image support at different time is not required;
(2) the method of the invention has very good applicability, effectiveness, stability and expansibility.
Drawings
FIG. 1 is a block flow diagram of an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention provides a remote sensing image thin cloud removing method considering element contour intensity, belongs to a single remote sensing image cloud removing method, and can well keep boundary and texture information without additional image support at different time.
In the embodiment of the invention, MATLAB R2017b is used for algorithm implementation.
FIG. 1 shows the steps of an embodiment of the present invention.
Step 1:
reading a remote sensing SAR image f with thin cloud by using an imread function, wherein the SAR image has speckle noise, namely the thin cloud.
Step 2:
and calculating element contour intensity values of the remote sensing images with the thin clouds. Assuming that the remote sensing image with thin clouds is f, the corresponding element profile intensity value is calculated using the following formula (1):
wherein,
respectively, forward difference operators in the horizontal and vertical directions.
The euclidean norm is denoted as i, j as pixels of the remote sensing image f, i 1, 2.
Then, using low pass filtering to k to obtain smoothed element contour intensity value
And step 3:
constructing a structural information extraction model considering the element outline strength, as follows:
wherein u is structural information of the remote sensing image f, lambda is a data item coefficient, and H is a Gaussian function with the mean value of 1;
the model is designed according to the maximum posterior probability and the prior characteristics of the processing task and the image.
And 4, step 4:
and solving the proposed structural information extraction model to obtain the structural information of the remote sensing image.
First, ζ is initialized to 0, u is initialized to f,
the transformation of formula (2) is a limited problem as follows:
② the augmented Lagrangian form for equation (3) is:
where ζ is the lagrange multiplier and r is the augmentation factor.
Equation (4) can be divided into the following two subproblems to solve:
and fourthly, fixing u and solving p for the neutron problem in the formula (5). For equation (5) can be simplified to:
for the above formula, there is a closed solution of the form:
for the neutron problem in the formula (6), p is fixed, u is solved, and fft2 and ifft2 in matlab are used for fast solving.
This can be simplified for equation (6):
a first derivative of the above equation can be found as follows:
equation (10) can be solved quickly by fast fourier transform.
Sixthly, updating the Lagrange multiplier.
And 5:
and constructing a thin cloud removal model to obtain a cloud-removed remote sensing image. First, the dark channel prior f is calculateddark. Then, calculating the remote sensing image after the thin cloud is removed:
wherein I is a remote sensing image after the thin cloud is removed, fdarkA dark channel prior, beta is a positive real number;
the model is designed according to the processing task and the prior characteristics of the image itself.
Step 6:
according to the solving process, using MATLAB to carry out algorithm realization;
and 7:
and outputting to obtain a thin cloud removing result, namely the remote sensing image after the thin cloud is removed.
The remote sensing image thin cloud removing method considering the element contour strength can effectively remove the thin cloud through strict test and verification, has very good applicability, effectiveness, stability and expansibility, realizes all the functions, and has the characteristics of applicability, effectiveness, stability and expansibility.
The present invention is not limited to the above-mentioned embodiments, and it will be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the present invention, and such modifications and enhancements are also considered to be within the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (10)
1. A remote sensing image thin cloud removing method considering element contour intensity is characterized by comprising the following steps:
p1: calculating element contour intensity value of remote sensing image with thin cloud
P2: constructing a structural information extraction model considering the outline strength of the elements;
p3: solving the proposed structural information extraction model to obtain structural information of the remote sensing image;
p4: and constructing a thin cloud removal model to obtain a cloud-removed remote sensing image.
2. The method for removing thin clouds from remote sensing images considering element contour intensity according to claim 1, wherein the method for calculating the element contour intensity value of the remote sensing image with thin clouds in the step P1 comprises the following steps:
p101, calculating corresponding element contour intensity values by using the following formula:
wherein,
forward difference operators in the horizontal direction and the vertical direction respectively;
i, j are pixels of the remote sensing image f, i is 1,2, and M, j is 1, 2.
P102, obtaining the smoothed element contour intensity value by using low-pass filtering on k
3. The method for removing the thin cloud of the remote sensing image considering the element contour strength as claimed in claim 1 or 2, wherein the method for constructing the structural information extraction model considering the element contour strength in step P2 includes:
wherein,
and (4) obtaining the smoothed element contour intensity value, wherein u is the structural information of the remote sensing image f, lambda is a data item coefficient, and H is a Gaussian function with the mean value of 1.
4. The method for removing the thin cloud of the remote sensing image considering the element contour intensity as claimed in claim 3, wherein the solving process of the structural information extraction model comprises the following steps:
p301, order
Formula (2) is converted as follows:
p302, augmented lagrange form for equation (3):
wherein, zeta is Lagrange multiplier, and r is augmentation coefficient;
p303 and the formula (4) are divided into the following two subproblems to solve:
p304, for equation (5), fixing u, solving for P; the simplification is as follows:
for equation (7) above, there is a closed solution of the form:
p305, for equation (6), fix P and solve u. The reduction for equation (6) is:
a first derivation of the above equation yields the following equation:
the formula (10) is rapidly solved through fast Fourier transform;
p306, updating a Lagrange multiplier;
5. the method for removing thin clouds from remote sensing images considering element contour intensity according to claim 1 or 4, wherein the thin cloud removal model in the step P4 is as follows:
wherein I is a remote sensing image after the thin cloud is removed, fdarkAnd the image is a dark channel prior, beta is a positive real number, and u is the structural information of the remote sensing image f.
6. A remote sensing image thin cloud removal system considering element contour intensity is characterized by comprising:
element contour strength calculation module: element contour intensity value for calculating remote sensing image with thin cloud
The structural information extraction model construction module: the system comprises a structural information extraction model for constructing a structural information extraction model considering the outline strength of elements;
a solving module: the remote sensing image extracting module is used for solving the proposed structural information extracting model to obtain structural information of the remote sensing image;
thin cloud removal model construction module: and the method is used for constructing a thin cloud removal model to obtain a cloud-removed remote sensing image.
7. The remote sensing image thin cloud removal system considering element contour intensity as claimed in claim 6, wherein the element contour intensity calculation module comprises:
a calculation unit: for calculating the corresponding element profile intensity value using the following equation:
wherein,
forward difference operators in the horizontal direction and the vertical direction respectively;
i, j are pixels of the remote sensing image f, i is 1,2, and M, j is 1, 2.
A filtering unit: for obtaining smoothed element contour intensity value by using low-pass filtering on k
8. The remote sensing image thin cloud removing system considering element contour intensity as claimed in claim 6 or 7, wherein the structural information extraction model building module builds the model by using the following formula:
wherein,
and (4) obtaining the smoothed element contour intensity value, wherein u is the structural information of the remote sensing image f, lambda is a data item coefficient, and H is a Gaussian function with the mean value of 1.
9. The remote sensing image thin cloud removal system considering element contour intensity as claimed in claim 8, wherein the solving module comprises:
a transformation unit: order to
Formula (2) is converted as follows:
an amplification unit: the augmented lagrange form for equation (3) is:
wherein, zeta is Lagrange multiplier, and r is augmentation coefficient;
a subproblem unit: equation (4) is divided into the following two sub-problems to solve:
a first solving unit: for equation (5), fixing u, solving for p; the simplification is as follows:
for equation (7) above, there is a closed solution of the form:
a second solving unit: for equation (6), p is fixed, and u is solved, which for equation (6) is simplified as:
a first derivation of the above equation yields the following equation:
the formula (10) is rapidly solved through fast Fourier transform;
an update unit: updating a Lagrange multiplier;
10. the remote sensing image thin cloud removal system considering element contour intensity as claimed in claim 6 or 9, wherein the thin cloud removal model building module builds the thin cloud removal model by using the following formula:
wherein I is a remote sensing image after the thin cloud is removed, fdarkAnd the image is a dark channel prior, beta is a positive real number, and u is the structural information of the remote sensing image f.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110727074.5A CN113298836B (en) | 2021-06-29 | 2021-06-29 | Remote sensing image thin cloud removing method and system considering element contour intensity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110727074.5A CN113298836B (en) | 2021-06-29 | 2021-06-29 | Remote sensing image thin cloud removing method and system considering element contour intensity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113298836A true CN113298836A (en) | 2021-08-24 |
| CN113298836B CN113298836B (en) | 2023-03-14 |
Family
ID=77329889
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110727074.5A Active CN113298836B (en) | 2021-06-29 | 2021-06-29 | Remote sensing image thin cloud removing method and system considering element contour intensity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113298836B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117218048A (en) * | 2023-11-07 | 2023-12-12 | 天津市测绘院有限公司 | Infrared and visible light image fusion method based on three-layer sparse smooth model |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509262A (en) * | 2011-10-17 | 2012-06-20 | 中煤地航测遥感局有限公司 | Method for removing thin cloud of remote sensing image |
| CN105389826A (en) * | 2015-12-23 | 2016-03-09 | 国家***第一海洋研究所 | High-resolution SAR remote sensing extraction method for coastline of coral island |
| CN105574826A (en) * | 2015-12-16 | 2016-05-11 | 中国科学院深圳先进技术研究院 | Thin cloud removing method of remote sensing image |
| CN108460739A (en) * | 2018-03-02 | 2018-08-28 | 北京航空航天大学 | A kind of thin cloud in remote sensing image minimizing technology based on generation confrontation network |
| CN109191400A (en) * | 2018-08-30 | 2019-01-11 | 中国科学院遥感与数字地球研究所 | A method of network, which is generated, using confrontation type removes thin cloud in remote sensing image |
| CN110047045A (en) * | 2019-04-10 | 2019-07-23 | 新疆大学 | Remove the method and device of thin cloud in remote sensing images |
| CN110287898A (en) * | 2019-06-27 | 2019-09-27 | 苏州中科天启遥感科技有限公司 | A kind of optical satellite remote sensing image cloud detection method of optic |
| CN112288640A (en) * | 2020-07-21 | 2021-01-29 | 中国地质大学（武汉） | Method, system and terminal equipment for inhibiting speckle noise of SAR image |
| CN112529788A (en) * | 2020-11-13 | 2021-03-19 | 北京航空航天大学 | Multispectral remote sensing image thin cloud removing method based on thin cloud thickness map estimation |
| CN112862729A (en) * | 2021-03-30 | 2021-05-28 | 湖北地信科技集团股份有限公司 | Remote sensing image denoising method based on characteristic curve guidance |
-
2021
- 2021-06-29 CN CN202110727074.5A patent/CN113298836B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509262A (en) * | 2011-10-17 | 2012-06-20 | 中煤地航测遥感局有限公司 | Method for removing thin cloud of remote sensing image |
| CN105574826A (en) * | 2015-12-16 | 2016-05-11 | 中国科学院深圳先进技术研究院 | Thin cloud removing method of remote sensing image |
| CN105389826A (en) * | 2015-12-23 | 2016-03-09 | 国家***第一海洋研究所 | High-resolution SAR remote sensing extraction method for coastline of coral island |
| CN108460739A (en) * | 2018-03-02 | 2018-08-28 | 北京航空航天大学 | A kind of thin cloud in remote sensing image minimizing technology based on generation confrontation network |
| CN109191400A (en) * | 2018-08-30 | 2019-01-11 | 中国科学院遥感与数字地球研究所 | A method of network, which is generated, using confrontation type removes thin cloud in remote sensing image |
| CN110047045A (en) * | 2019-04-10 | 2019-07-23 | 新疆大学 | Remove the method and device of thin cloud in remote sensing images |
| CN110287898A (en) * | 2019-06-27 | 2019-09-27 | 苏州中科天启遥感科技有限公司 | A kind of optical satellite remote sensing image cloud detection method of optic |
| CN112288640A (en) * | 2020-07-21 | 2021-01-29 | 中国地质大学（武汉） | Method, system and terminal equipment for inhibiting speckle noise of SAR image |
| CN112529788A (en) * | 2020-11-13 | 2021-03-19 | 北京航空航天大学 | Multispectral remote sensing image thin cloud removing method based on thin cloud thickness map estimation |
| CN112862729A (en) * | 2021-03-30 | 2021-05-28 | 湖北地信科技集团股份有限公司 | Remote sensing image denoising method based on characteristic curve guidance |
Non-Patent Citations (2)
| Title |
|---|
| 贺辉 等: ""自适应滤波的高分辨率遥感影像薄云去除算法"", 《地球信息科学学报》 * |
| 陈奋 等: ""基于无抽样小波的遥感影像薄云检测与去除"", 《武汉大学学报 信息科学版》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117218048A (en) * | 2023-11-07 | 2023-12-12 | 天津市测绘院有限公司 | Infrared and visible light image fusion method based on three-layer sparse smooth model |
| CN117218048B (en) * | 2023-11-07 | 2024-03-08 | 天津市测绘院有限公司 | Infrared and visible light image fusion method based on three-layer sparse smooth model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113298836B (en) | 2023-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108921799B (en) | Remote sensing image thin cloud removing method based on multi-scale collaborative learning convolutional neural network | |
| US20220044375A1 (en) | Saliency Map Enhancement-Based Infrared and Visible Light Fusion Method | |
| Huang et al. | Cloud removal from optical satellite imagery with SAR imagery using sparse representation | |
| KR101580585B1 (en) | Method for data fusion of panchromatic and thermal-infrared images and Apparatus Thereof | |
| CN111145131A (en) | Infrared and visible light image fusion method based on multi-scale generation type countermeasure network | |
| CN103886559B (en) | Spectrum image processing method | |
| CN113222877B (en) | Infrared and visible light image fusion method and application thereof in airborne photoelectric video | |
| CN103914813A (en) | Colorful haze image defogging and illumination compensation restoration method | |
| Teke | Satellite image processing workflow for RASAT and Göktürk-2 | |
| CN110035239B (en) | Multi-integral time infrared image fusion method based on gray scale-gradient optimization | |
| KR101928391B1 (en) | Method and apparatus for data fusion of multi spectral image and radar image | |
| CN113112533B (en) | SAR-multispectral-hyperspectral integrated fusion method based on multiresolution analysis | |
| Ackar et al. | A review on image enhancement techniques | |
| Kang et al. | Fog model-based hyperspectral image defogging | |
| CN116935214B (en) | Space-time spectrum fusion method for satellite multi-source remote sensing data | |
| Mitianoudis et al. | Optimal contrast correction for ICA-based fusion of multimodal images | |
| JP7152131B2 (en) | Image processing device, image processing method and program | |
| CN113298836B (en) | Remote sensing image thin cloud removing method and system considering element contour intensity | |
| CN104050638A (en) | Saliency method infrared small target enhancing method combined with scale optimization | |
| Luo et al. | An evolutionary shadow correction network and a benchmark UAV dataset for remote sensing images | |
| CN105719254A (en) | Image denoising method and system | |
| CN112966612B (en) | Method for extracting remote sensing image of arctic sea ice based on Newton integral neurodynamics | |
| Shi et al. | A novel thin cloud removal method based on multiscale dark channel prior (MDCP) | |
| CN106971385B (en) | A kind of aircraft Situation Awareness multi-source image real time integrating method and its device | |
| Fu et al. | Sparsity constrained fusion of hyperspectral and multispectral images |
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 |