CN111553922A - Automatic cloud detection method for satellite remote sensing image - Google Patents
Automatic cloud detection method for satellite remote sensing image Download PDFInfo
- Publication number
- CN111553922A CN111553922A CN202010224301.8A CN202010224301A CN111553922A CN 111553922 A CN111553922 A CN 111553922A CN 202010224301 A CN202010224301 A CN 202010224301A CN 111553922 A CN111553922 A CN 111553922A
- Authority
- CN
- China
- Prior art keywords
- cloud
- band
- cim
- image
- index
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000002310 reflectometry Methods 0.000 claims abstract description 25
- 239000011159 matrix material Substances 0.000 claims abstract description 17
- 238000013139 quantization Methods 0.000 claims abstract description 7
- 238000004458 analytical method Methods 0.000 claims abstract description 5
- 238000001914 filtration Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000003595 spectral effect Effects 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 238000001228 spectrum Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000001747 pupil Anatomy 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 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
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- 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
- G06T2207/30192—Weather; Meteorology
-
- 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 relates to an automatic cloud detection method for satellite remote sensing images, which comprises the following steps: (1) converting the pixel quantization value of each wave band of the satellite remote sensing image into the atmospheric layer top reflectivity; (2) calculating HOTM index, CIM index and VBRM index of the image based on the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image to obtain corresponding HOTM index image, CIM index image and VBRM index image; (3) segmenting the HOTM index image by using a threshold method to obtain a possible cloud coverage area; (4) automatically segmenting a CIM index image of a possible cloud coverage area of the image by adopting a maximum inter-class variance method to obtain a preliminary cloud coverage area; (5) and removing non-cloud highlight ground objects in the primary cloud coverage area, then performing neighborhood analysis, eliminating cloud internal cavities and filtering isolated points, finally obtaining a cloud mask image binary matrix, and completing automatic cloud detection of the image. The method can effectively improve the thin cloud extraction precision of the high-resolution six-number image and complete automatic cloud detection.
Description
Technical Field
The invention particularly relates to an automatic cloud detection method for satellite remote sensing images, and belongs to the field of remote sensing image processing.
Background
The optical remote sensing image is important basic data for observing the earth surface and acquiring earth surface information. The annual average cloud amount of the whole world is about 66%, about 60% of areas of the optical satellite remote sensing images shot conventionally are covered by clouds, and the coverage of the clouds on the remote sensing images shields or reduces the acquisition of effective information of the earth surface, so that cloud detection is carried out before earth surface classification, target extraction, quantitative inversion and other work are carried out on the remote sensing images, and the generation of an accurate cloud mask is of great significance.
Optical image cloud detection has been used for many years, but only images in visible light and near infrared bands are detected, and due to the fact that medium wave and thermal infrared band detection are lacked, thin cloud errors are large, most of the existing methods adopt a threshold value method for the images only in the visible light and near infrared bands, and thin cloud detection accuracy is not high. The Wuhan university CN105354865B patent discloses a multispectral remote sensing satellite image automatic cloud detection method and system, the method aims at visible light and near-infrared 4-waveband data, changes color space, extracts a primary detection cloud object by a threshold value, then carries out error elimination on a coarse detection result based on image texture information, and finally adopts an edge seed expansion mode to accurately extract a cloud layer, so that the extraction precision of attached thin clouds around the thick clouds is improved. However, the method mainly has the following problems: the texture extraction method has low precision when the clouds are sparsely distributed, only improves the extraction precision of the thin clouds attached to the thick clouds, and if the thin clouds are independent, the precision extraction of the method is disabled.
Disclosure of Invention
The technical problem solved by the invention is as follows: the method overcomes the defects of the prior art, and provides the automatic cloud detection method for the satellite remote sensing image using the red-edge wave band so as to improve the automation degree and the precision of the image cloud detection.
The technical scheme of the invention is as follows: a satellite remote sensing image automatic cloud detection method comprises the following steps:
(1) converting the pixel quantization value of each wave band of the satellite remote sensing image into the atmospheric layer top reflectivity;
(2) calculating a cloud index haze optimization transformation index HOTM, an average reflectivity index CIM and a high brightness index VBRM of the image based on the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image, and obtaining a corresponding HOTM index image, a CIM index image and a VBRM index image;
(3) dividing the HOTM index image to obtain a possible cloud coverage area;
(4) automatically segmenting the CIM index image of the possible cloud coverage area to obtain a preliminary cloud coverage area;
(5) and removing non-cloud highlight ground objects in the primary cloud coverage area, then performing neighborhood analysis, eliminating cloud internal cavities and filtering isolated points, finally obtaining a cloud mask image binary matrix, and completing automatic cloud detection of the image.
The specific steps of the step (1) are as follows:
(1.1) converting pixel quantization values of all wave bands of the satellite remote sensing image into radiance by using a radiometric calibration coefficient;
and (1.2) calculating the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image according to the radiance of each wave band of the satellite remote sensing image.
When the satellite remote sensing image is a high-resolution six-size satellite WFV image, the band number and the band spectrum range of the high-resolution six-size WFV image are as follows:
| band number | Spectral range (mum) | Band name |
| 1 | 0.45-0.52 | Blue wave band |
| 2 | 0.52-0.59 | Green band |
| 3 | 0.63-0.69 | Red waveSegment of |
| 4 | 0.77-0.89 | Near infrared band |
| 5 | 0.69-0.73 | Red edge band 1 |
| 6 | 0.73-0.77 | Red edge band 2 |
| 7 | 0.40-0.45 | Coastal wave band |
| 8 | 0.59-0.63 | Yellow orange band |
The specific calculation formula of the HOTM index is as follows:
HOTM＝B1-(B3+B5+B6)/6
wherein, B1、B3、B5And B6The atmospheric layer top reflectivities of band 1, band 3, band 5, and band 6 are shown, respectively.
The specific calculation formula of the CIM index is as follows:
CIM＝(B1+B2+B3+B4+B5+B6+B8)/6
wherein, B1、B2、B3、B4、B5、B6And B8Respectively represent the magnitudes of band 1, band 2, band 3, band 4, band 5, band 6 and band 8Air layer top reflectance.
The specific calculation formula of the VBRM index is as follows:
wherein, B1、B2、B3、B4、B5、B6And B8Respectively represent the atmospheric layer top reflectivity of band 1, band 2, band 3, band 4, band 5, band 6 and band 8.
The step (2) also converts the value of the CIM index image into an N-bit fixed point numerical range, and the formula is as follows:
CIMunitN＝(CIM-CIMmin)×(2N-1)/(CIMmax-CIMmin)
wherein, CIMunitNIs CIM index value after conversion, CIM is CIM index value before conversion, CIMminIs the minimum value on the CIM index image, CIMmaxIs the maximum value on the CIM index image.
The possible cloud coverage area of the step (3) is determined by the following method:
PCloud(x,y)＝Bool(HOTM(x,y)≥Th1)
wherein, PCloud (x, y) is a binary matrix of a possible cloud coverage area, book (·) is a boolean function, which indicates that the element value satisfying the internal condition of the bracket is 1, otherwise, 0, (x, y) is the position of the pixel, and Th1 is a preset threshold.
The calculation formula for determining the coverage area of the initial cloud in the step (4) is as follows:
Cloudv1(x,y)＝(Bool((CIMunitN(x,y)≥K)&&PCloud(x,y))
wherein, Cloudv1The binary matrix is a binary matrix of a preliminary cloud coverage area, Bool (·) is a Boolean function, an element value which indicates that the internal condition of brackets is met is 1, the preliminary judgment is cloud, otherwise, the element value is 0, the preliminary judgment is clear sky, (x, y) are positions of pixels, and K is a threshold value automatically obtained by an OTSU method; CIMunitN is a method for converting CIM exponential image values into N-bit fixed point value rangesCIM index image after enclosing;&&indicating an and operation, and N takes the value of 8 or 16.
The method is characterized in that the preset preliminary cloud coverage threshold K is a threshold value automatically obtained by a maximum inter-class variance method.
The formula for removing possible non-cloud highlight land features in the step (5) is as follows:
Cloudv2(x,y)＝(Bool(VBRM(x,y)≥Th2)&&Cloudv1(x, y)) brackets
Wherein, Cloudv2The method is a cloud coverage binary matrix obtained after non-cloud highlight ground objects in a preliminary cloud coverage area are removed, Bool (·) is a Boolean function, an element value which meets the internal condition of brackets is 1, the preliminary judgment is that the non-cloud highlight ground objects are obtained, otherwise, the Bool (·) is 0, cloud is obtained, (x, y) are positions of pixels, and Th2 is a preset VBRM threshold;&&indicating an and operation.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention comprehensively utilizes three improved cloud index HOTM, CIM and VBRM index image information, the three indexes enhance the difference of thick cloud, thin cloud and ground features, and the detection precision of the cloud (particularly the thin cloud) is improved.
(2) The invention combines the spectral information characteristics of the high-resolution six-number image, improves the calculation formula of 3 cloud indexes, utilizes the difference of two red-edge wave bands and blue wave bands of the high-resolution six-number WFV image in spectral response to the thin cloud, and improves the extraction precision of the thin cloud.
(3) The method combines the automatic algorithm of image segmentation, the threshold value of key segmentation is automatically determined according to the algorithm according to the image, the method is different from image to image, the adaptability is good, and the calculation and the solution are simple and effective.
(4) The high-resolution six-size WFV image has a good recognition effect on thin clouds, and the overall cloud detection precision can reach 90%.
Drawings
FIG. 1 is a flowchart of an automatic cloud detection method for satellite remote sensing images according to an embodiment of the present invention;
fig. 2(a) is a partial view of a WFV image with high score six product number 1119860129 imaged on 12 days 10 months 2018;
fig. 2(b) shows the cloud detection result of the image of fig. 2 (a).
Detailed Description
The invention is further illustrated by the following examples.
As shown in fig. 1, an automatic cloud detection method for satellite remote sensing images includes the following steps:
(1) converting the pixel quantization value of each wave band of the satellite remote sensing image into the atmospheric layer top reflectivity;
the method comprises the following specific steps:
(1.1) converting pixel quantization values (DN values) of all wave bands of the satellite remote sensing image into radiance by using a radiometric calibration coefficient, wherein the specific formula is as follows:
Li＝Gaini×DNi+Biasi，i＝1～M
wherein L isiIs the equivalent radiance (W.m) at the entrance pupil of the satellite loading band-2·Sr-1·μm-1) I.e. radiance; gainiAnd BiasiAnd respectively, the gain and the offset of the scaling coefficient are obtained by an image file or a data provider, and M is the number of wave bands of the satellite remote sensing image.
(1.2) calculating the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image according to the radiance of each wave band of the satellite remote sensing image, wherein the specific formula is as follows:
wherein, BiIs the atmospheric layer top reflectivity (dimensionless), i is the band number, pi is the constant, LiFor radiance, D is the distance between the day and the earth (astronomical units), ESUN is the average solar spectral irradiance (W.m) at the top of the atmospheric layer-2·sr-1·μm-1) Theta is the zenith angle of the sun, D, ESUN and theta can be obtained through image metafiles released by satellite authorities.
(2) Calculating a cloud index haze optimization transformation index HOTM, an average reflectivity index CIM and a high brightness index VBRM of the image based on the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image, and obtaining a corresponding HOTM index image, a CIM index image and a VBRM index image;
when the satellite remote sensing image is a high-resolution six-size satellite WFV image, the wave band numbers and the wave band spectrum ranges of the high-resolution six-size WFV image are shown in the following table:
TABLE 1 high-resolution six WFV image band number and band spectral range
The method for obtaining the corresponding index images by calculating three improved cloud index haze optimization transformation indexes (HOTM), average reflectivity index (CIM) and high brightness index (VBRM) based on the atmospheric layer top reflectivity of 7 wave bands of the high-resolution six-satellite WFV images is as follows:
the specific calculation formula of the HOTM index is as follows:
HOTM＝B1-(B3+B5+B6)/6
wherein, B1、B3、B5And B6The atmospheric layer top reflectivities of band 1, band 3, band 5, and band 6 are shown, respectively. B is5And B6The red-edge band of the high-resolution six-satellite WFV image.
The specific calculation formula of the CIM index is as follows:
CIM＝(B1+B2+B3+B4+B5+B6+B8)/6
wherein, B1、B2、B3、B4、B5、B6And B8Respectively represent the atmospheric layer top reflectivity of band 1, band 2, band 3, band 4, band 5, band 6 and band 8.
The specific calculation formula of the VBRM index is as follows:
wherein, B1、B2、B3、B4、B5、B6And B8Respectively represent the atmospheric layer top reflectivity of band 1, band 2, band 3, band 4, band 5, band 6 and band 8.
For convenience of operation and reduction of data processing amount, the value of the CIM index image may be converted into an N-bit fixed point numerical range, and the formula is as follows:
CIMunitN＝(CIM-CIMmin)×(2N-1)/(CIMmax-CIMmin)
wherein, CIMunitNIs CIM index value after conversion, CIM is CIM index value before conversion, CIMminIs the minimum value on the CIM index image, CIMmaxIs the maximum value on the CIM index image.
(3) Dividing the HOTM index image by using a threshold method to obtain a possible cloud coverage area (including a part of clear sky pixels);
the possible cloud coverage area is determined by the following method:
PCloud(x,y)＝Bool(HOTM(x,y)≥Th1)
wherein, PCloud (x, y) is a binary matrix of a possible cloud coverage area, book (·) is a boolean function, which indicates that the element value satisfying the internal condition of the bracket is 1, otherwise, 0, (x, y) is the position of the pixel, and Th1 is a preset threshold value of 0.12. Th1 ranges from 0.08-0.16, ensuring that all clouds (including thin clouds) are within the extracted possible cloud coverage area, while removing part of the background terrain.
(4) Automatically dividing CIM index images of possible cloud coverage areas by adopting a maximum inter-class variance method to obtain a preliminary cloud coverage area;
the maximum inter-class variance method (OTSU method for short) is a classical adaptive threshold determination method, where a threshold K divides an image into a background and a target, and K is determined by maximizing the inter-class variance between the background and the target, and the specific formula of the inter-class variance is as follows:
g＝ω1×ω2×(μ1-μ2)2
wherein g is the inter-class variance, ω1The average gray scale of the foreground pixel points is mu1，ω2The average gray scale of the background pixel point is mu as the proportion of the background pixel point occupying the pixel point of the region to be segmented2(ii) a And finding the maximum value of the inter-class variance by adopting a traversal method, wherein the corresponding threshold value K is the threshold value of the segmented image.
The calculation formula for determining the coverage area of the primary cloud is as follows:
Cloudv1(x,y)＝Bool((CIMunitN(x,y)≥K)&&PCloud(x,y)
wherein, Cloudv1The binary matrix is a binary matrix of a preliminary cloud coverage area, Bool (·) is a Boolean function, an element value which indicates that the internal condition of brackets is met is 1, the preliminary judgment is cloud, otherwise, the element value is 0, the preliminary judgment is clear sky, (x, y) are positions of pixels, and K is a threshold value automatically obtained by an OTSU method; CIMunitN is CIM index image obtained by converting CIM index image into N-bit fixed point value range;&&indicating an and operation with N taking the value 8.
The preset preliminary cloud coverage threshold K is a threshold value automatically obtained by a maximum inter-class variance method.
(5) And removing non-cloud highlight ground objects in the primary cloud coverage area, then performing neighborhood analysis, eliminating cloud internal cavities and filtering isolated points, finally obtaining a cloud mask image binary matrix, and completing automatic cloud detection of the image.
The blue wave band and the red and red wave bands have different spectral responses to haze and thin cloud, and the HOTM index utilizes the wave bands to enhance the difference between cloud and ground features, can be used for distinguishing cloud (thin cloud and thick cloud) and clear sky pixels, but can contain some high-brightness ground features.
The formula for removing possible non-cloud highlights is as follows:
Cloudv2(x,y)＝(Bool(VBRM(x,y)≥Th2)&&Cloudv1(x,y))
wherein, Cloudv2The method is a cloud coverage binary matrix obtained after non-cloud highlight ground objects in a preliminary cloud coverage area are removed, Bool (·) is a Boolean function, an element value which meets the internal condition of brackets is 1, the preliminary judgment is that the non-cloud highlight ground objects are obtained, otherwise, the Bool (·) is 0, cloud is obtained, (x, y) are positions of pixels, and Th2 is a preset VBRM threshold;&&indicating an and operation. Th2 is 0.7. Th2 ranges from 0.6 to 0.8, ensuring that as much highlight as possible (not all) is removed by the threshold, but at the same time no clouds are present.
To CloudV2Performing neighborhood analysis on the binary matrix, analyzing by using a 3 x 3 pixel template, and resetting the pixel as a cloud pixel (with a setting value of 1) if more than 5 adjacent pixels (actually 8) of the non-cloud pixel are cloud pixels; in addition, filtering out isolated points, and removing cloud targets (the set value is 0) with less than 5 pixels; finally obtaining a Cloud mask image binary matrix CloudV3And completing the automatic cloud detection of the image.
Example 1:
in the following, cloud detection was performed by using the present invention, taking a WFV image part (3072 × 3072 pixels) with a product number of 1119860129, which is high score six, imaged in 12 days 10 and 12 months 2018 as an example, according to a specific embodiment of the present invention. The image before detection is as shown in fig. 2(a), which comprises 3072 × 3072 pixels, and the 3 rd wave band red wave band gray scale display; as shown in fig. 2(b), the detection result is that the gray scale of the cloud mask binary image is displayed, white is cloud, and black is clear sky. As can be seen from the figure: the method has a good recognition effect on the high-resolution six-size WFV image thin clouds, and the overall cloud detection precision can reach 90%.
FIG. 2(a) illustrates various types of underlying surfaces, including farmland, water, artificial ground, etc.; the cloud is distributed dispersedly or intensively and has thick cloud and thin cloud; the cloud detection result in fig. 2(b) shows that the overall cloud detection effect is good, thick clouds are detected completely, most thin clouds are detected, a small amount of transparent thin clouds are not identified, and the cloud boundary of the detection result is accurate and smooth.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
Claims (11)
1. An automatic cloud detection method for satellite remote sensing images is characterized by comprising the following steps:
(1) converting the pixel quantization value of each wave band of the satellite remote sensing image into the atmospheric layer top reflectivity;
(2) calculating a cloud index haze optimization transformation index HOTM, an average reflectivity index CIM and a high brightness index VBRM of the image based on the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image, and obtaining a corresponding HOTM index image, a CIM index image and a VBRM index image;
(3) dividing the HOTM index image to obtain a possible cloud coverage area;
(4) automatically segmenting the CIM index image of the possible cloud coverage area to obtain a preliminary cloud coverage area;
(5) and removing non-cloud highlight ground objects in the primary cloud coverage area, then performing neighborhood analysis, eliminating cloud internal cavities and filtering isolated points, finally obtaining a cloud mask image binary matrix, and completing automatic cloud detection of the image.
2. The automatic cloud detection method for the satellite remote sensing images according to claim 1, wherein the specific steps in the step (1) are as follows:
(1.1) converting pixel quantization values of all wave bands of the satellite remote sensing image into radiance by using a radiometric calibration coefficient;
and (1.2) calculating the atmospheric layer top reflectivity of each wave band of the satellite remote sensing image according to the radiance of each wave band of the satellite remote sensing image.
3. The automatic cloud detection method for the satellite remote sensing images according to claim 1, wherein when the satellite remote sensing images are high-resolution six-size satellite WFV images, the wave band numbers and the wave band spectrum ranges of the high-resolution six-size WFV images are as follows:
4. The automatic cloud detection method for the satellite remote sensing images according to claim 3, wherein a specific calculation formula of the HOTM index is as follows:
HOTM＝B1-(B3+B5+B6)/6
wherein, B1、B3、B5And B6The atmospheric layer top reflectivities of band 1, band 3, band 5, and band 6 are shown, respectively.
5. The automatic cloud detection method for the satellite remote sensing images according to claim 3, wherein a specific calculation formula of the CIM index is as follows:
CIM＝(B1+B2+B3+B4+B5+B6+B8)/6
wherein, B1、B2、B3、B4、B5、B6And B8Respectively represent the atmospheric layer top reflectivity of band 1, band 2, band 3, band 4, band 5, band 6 and band 8.
6. The automatic cloud detection method for satellite remote sensing images according to claim 3, wherein the specific calculation formula of the VBRM index is as follows:
wherein, B1、B2、B3、B4、B5、B6And B8Respectively represent the atmospheric layer top reflectivity of band 1, band 2, band 3, band 4, band 5, band 6 and band 8.
7. The automatic cloud detection method for satellite remote sensing images according to claim 3, wherein the step (2) further converts the value of the CIM index image into an N-bit fixed point numerical range, and the formula is as follows:
CIMunitN＝(CIM-CIMmin)×(2N-1)/(CIMmax-CIMmin)
wherein, CIMunitNIs CIM index value after conversion, CIM is CIM index value before conversion, CIMminIs the minimum value on the CIM index image, CIMmaxIs the maximum value on the CIM index image.
8. The automatic cloud detection method for the satellite remote sensing images according to claim 3, wherein the possible cloud coverage area is determined by the following method:
PCloud(x,y)＝Bool(HOTM(x,y)≥Th1)
wherein, PCloud (x, y) is a binary matrix of a possible cloud coverage area, book (·) is a boolean function, which indicates that the element value satisfying the internal condition of the bracket is 1, otherwise, 0, (x, y) is the position of the pixel, and Th1 is a preset threshold.
9. The automatic cloud detection method for the satellite remote sensing images according to claim 7, wherein a calculation formula for determining the coverage area of the initial cloud is as follows:
Cloudv1(x,y)＝(Bool((CIMunitN(x,y)≥K)&&PCloud(x,y))
wherein, Cloudv1Is a binary matrix of a preliminary cloud coverage area, Bool (·) is a Boolean function, the element value which indicates that the internal condition of brackets is met is 1, indicates that the cloud is preliminarily judged, otherwise, is 0, indicates that the clear sky is preliminarily judged, (x, y) is the position of a pixel, and K is an OTSU methodAutomatically obtaining a threshold value; CIMunitNConverting the value of the CIM index image into the CIM index image after the N-bit fixed point numerical range;&&indicating an and operation, and N takes the value of 8 or 16.
10. The automatic cloud detection method for the satellite remote sensing images according to claim 9, wherein the preset preliminary cloud coverage threshold K is a threshold value automatically obtained by a variance method between maximum classes.
11. The method for automatically detecting the cloud of the satellite remote sensing image using the red-edge band according to claim 3, wherein the formula for removing the possible non-cloud highlight feature in the step (5) is as follows:
Cloudv2(x,y)＝Bool(VBRM(x,y)≥Th2)&&Cloudv1(x,y)
wherein, Cloudv2The method is a cloud coverage binary matrix obtained after non-cloud highlight ground objects in a preliminary cloud coverage area are removed, Bool (·) is a Boolean function, an element value which meets the internal condition of brackets is 1, the preliminary judgment is that the non-cloud highlight ground objects are obtained, otherwise, the Bool (·) is 0, cloud is obtained, (x, y) are positions of pixels, and Th2 is a preset VBRM threshold;&&indicating an and operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010224301.8A CN111553922B (en) | 2020-03-26 | 2020-03-26 | Automatic cloud detection method for satellite remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010224301.8A CN111553922B (en) | 2020-03-26 | 2020-03-26 | Automatic cloud detection method for satellite remote sensing image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111553922A true CN111553922A (en) | 2020-08-18 |
| CN111553922B CN111553922B (en) | 2023-12-26 |
Family
ID=72002395
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010224301.8A Active CN111553922B (en) | 2020-03-26 | 2020-03-26 | Automatic cloud detection method for satellite remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111553922B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113284066A (en) * | 2021-05-28 | 2021-08-20 | 生态环境部卫星环境应用中心 | Automatic cloud detection method and device for remote sensing image |
| CN113379759A (en) * | 2021-05-11 | 2021-09-10 | 中国资源卫星应用中心 | Automatic water body extraction method for optical remote sensing satellite image |
| CN113643244A (en) * | 2021-07-23 | 2021-11-12 | 中国资源卫星应用中心 | Rapid detection method for flare of water body of optical remote sensing satellite image |
| CN113838065A (en) * | 2021-09-23 | 2021-12-24 | 江苏天汇空间信息研究院有限公司 | Automatic cloud removing method based on image markers |
| CN114792322A (en) * | 2022-06-23 | 2022-07-26 | 中国科学院、水利部成都山地灾害与环境研究所 | Method for detecting cloud and cloud shadow of mountain domestic high-resolution satellite image |
| CN117315455A (en) * | 2023-01-31 | 2023-12-29 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Remote sensing cloud detection method considering cloud information characterization index and geometric form characteristics |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854513A (en) * | 2012-09-05 | 2013-01-02 | 环境保护部卫星环境应用中心 | Cloud detection method of CCD (charge coupled device) data of environment first satellite HJ-1A/B |
| US8594375B1 (en) * | 2010-05-20 | 2013-11-26 | Digitalglobe, Inc. | Advanced cloud cover assessment |
| CN105678777A (en) * | 2016-01-12 | 2016-06-15 | 武汉大学 | Feature-combined optical satellite image cloud and cloud shadow detection method |
| US20170161584A1 (en) * | 2015-12-07 | 2017-06-08 | The Climate Corporation | Cloud detection on remote sensing imagery |
-
2020
- 2020-03-26 CN CN202010224301.8A patent/CN111553922B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8594375B1 (en) * | 2010-05-20 | 2013-11-26 | Digitalglobe, Inc. | Advanced cloud cover assessment |
| CN102854513A (en) * | 2012-09-05 | 2013-01-02 | 环境保护部卫星环境应用中心 | Cloud detection method of CCD (charge coupled device) data of environment first satellite HJ-1A/B |
| US20170161584A1 (en) * | 2015-12-07 | 2017-06-08 | The Climate Corporation | Cloud detection on remote sensing imagery |
| CN105678777A (en) * | 2016-01-12 | 2016-06-15 | 武汉大学 | Feature-combined optical satellite image cloud and cloud shadow detection method |
Non-Patent Citations (1)
| Title |
|---|
| 束美艳;顾晓鹤;孙林;朱金山;陈婷婷;王凯;王权;杨贵军;: "先验终端像元库支持下的GF-4多光谱影像自动云检测" * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379759A (en) * | 2021-05-11 | 2021-09-10 | 中国资源卫星应用中心 | Automatic water body extraction method for optical remote sensing satellite image |
| CN113284066A (en) * | 2021-05-28 | 2021-08-20 | 生态环境部卫星环境应用中心 | Automatic cloud detection method and device for remote sensing image |
| CN113643244A (en) * | 2021-07-23 | 2021-11-12 | 中国资源卫星应用中心 | Rapid detection method for flare of water body of optical remote sensing satellite image |
| CN113643244B (en) * | 2021-07-23 | 2024-05-21 | 中国资源卫星应用中心 | Quick detection method for flare of optical remote sensing satellite image water body |
| CN113838065A (en) * | 2021-09-23 | 2021-12-24 | 江苏天汇空间信息研究院有限公司 | Automatic cloud removing method based on image markers |
| CN114792322A (en) * | 2022-06-23 | 2022-07-26 | 中国科学院、水利部成都山地灾害与环境研究所 | Method for detecting cloud and cloud shadow of mountain domestic high-resolution satellite image |
| CN117315455A (en) * | 2023-01-31 | 2023-12-29 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Remote sensing cloud detection method considering cloud information characterization index and geometric form characteristics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111553922B (en) | 2023-12-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111553922A (en) | Automatic cloud detection method for satellite remote sensing image | |
| Sandmeier et al. | A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain | |
| WO2020015326A1 (en) | Remote sensing image cloud shadow detection method supported by earth surface type data | |
| CN108280812B (en) | Image enhancement-based fire passing area extraction method | |
| CN112183209A (en) | Regional crop classification method and system based on multi-dimensional feature fusion | |
| WO2022252242A1 (en) | Multispectral image-based water pollution area identification method and system | |
| CN105526874B (en) | A kind of oil film thickness recognition methods based on spectral signature parameter | |
| CN110765934B (en) | Geological disaster identification method based on multi-source data fusion | |
| CN107688777B (en) | Urban green land extraction method for collaborative multi-source remote sensing image | |
| CN112285710A (en) | Multi-source remote sensing reservoir water storage capacity estimation method and device | |
| CN106971397B (en) | Based on the city high-resolution remote sensing image dividing method for improving JSEG algorithms | |
| JP2019537151A (en) | Image processing apparatus, image processing method, and image processing program | |
| CN113379759A (en) | Automatic water body extraction method for optical remote sensing satellite image | |
| CN110988909A (en) | TLS-based vegetation coverage determination method for sandy land vegetation in alpine and fragile areas | |
| CN111415309A (en) | High-resolution remote sensing image atmospheric correction method based on minimum reflectivity method | |
| CN115271217A (en) | Wheat yield prediction method based on multi-source remote sensing data of unmanned aerial vehicle | |
| CN112669363B (en) | Method for measuring three-dimensional green space of urban green space | |
| WO2019184269A1 (en) | Landsat 8 snow-containing image-based cloud detection method | |
| Feng et al. | A hierarchical extraction method of impervious surface based on NDVI thresholding integrated with multispectral and high-resolution remote sensing imageries | |
| CN114266958A (en) | Cloud platform based mangrove remote sensing rapid and accurate extraction method | |
| CN117575953B (en) | Detail enhancement method for high-resolution forestry remote sensing image | |
| CN111611965A (en) | Method for extracting land surface water body based on Sentinel-2 image | |
| CN109377476B (en) | Method and device for acquiring dynamic threshold of cloud detection characteristic parameter of remote sensing image | |
| CN114117908A (en) | High-precision ASI sea ice density inversion algorithm for data correction based on CGAN | |
| CN116682024A (en) | Rapid cloud detection method based on four-band remote sensing image |
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 |