CN111947773B - Remote sensing image path radiation estimation method - Google Patents
Remote sensing image path radiation estimation method Download PDFInfo
- Publication number
- CN111947773B CN111947773B CN202010842808.XA CN202010842808A CN111947773B CN 111947773 B CN111947773 B CN 111947773B CN 202010842808 A CN202010842808 A CN 202010842808A CN 111947773 B CN111947773 B CN 111947773B
- Authority
- CN
- China
- Prior art keywords
- pixels
- shadow
- remote sensing
- sensing image
- radiation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000005855 radiation Effects 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005286 illumination Methods 0.000 claims abstract description 30
- 230000003595 spectral effect Effects 0.000 claims abstract description 13
- 238000003384 imaging method Methods 0.000 claims description 6
- 238000002310 reflectometry Methods 0.000 abstract description 6
- 238000005259 measurement Methods 0.000 abstract description 2
- 238000012545 processing Methods 0.000 abstract description 2
- 238000000605 extraction Methods 0.000 description 4
- 238000012937 correction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 210000001747 pupil Anatomy 0.000 description 1
- 238000002834 transmittance Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J1/00—Photometry, e.g. photographic exposure meter
- G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a remote sensing image path radiation estimation method, and belongs to the technical field of intelligent remote sensing image processing. The method utilizes the radiation energy difference of the shadow area and the illumination area on the remote sensing image to image, and uses a regression method to calculate the path radiation of each wave band. Firstly, extracting shadow pixels on a remote sensing image; then, a shadow pixel and an illumination pixel with the same earth surface coverage and the distance of 4 are searched in the horizontal direction by utilizing the spectral included angle and the spectral Euclidean distance measurement; and finally, obtaining the range radiation value of each wave band by using a regression method. The method does not need to assume that a dark target with the reflectivity close to 0 exists on the remote sensing image, and is suitable for radiation estimation of all shaded remote sensing image paths.
Description
Technical Field
The invention belongs to the technical field of remote sensing image intelligent processing, and particularly relates to a method for estimating radiation of each wave range at the imaging moment of a remote sensing image, namely a method for estimating radiation of a remote sensing image range.
Background
The radiance at the entrance pupil of the atmospheric dome sensor includes the energy reflected by the ground objects and the energy scattered upward by the atmosphere (i.e., path radiation), and the path radiation reduces the contrast of the remote sensing image, so the atmospheric correction is generally needed to remove the influence of the atmosphere. Simple atmospheric correction needs to take into account the path radiation of each band of the remote sensing image.
The dark target method (DOS) is the most commonly used range radiance estimation method. The method assumes that a dark target with the earth surface reflectivity of almost 0 exists in the remote sensing image, and the DN value energy of the target on the remote sensing image is from range radiation, so the minimum value of each wave band is the range radiation. Generally, a large area of clear water absorbs strongly in the near infrared, with a reflectivity close to 0. However, in practice, this method often fails, as for shadows of dense vegetation, which are generally considered dark targets, but which still receive atmospheric scattered energy, range radiation is overestimated. To solve the problem, a multiband regression algorithm (MBR) can estimate the range radiation of another waveband according to the range radiation of a certain waveband; the latter researchers have derived Covariance Matrix Method (CMM) from MBR improvement. However, both the MBR and CMM methods need to accurately estimate the range radiation of a certain band in advance, and still do not fundamentally solve the problem of range radiation estimation.
Disclosure of Invention
In view of the above, the invention provides a remote sensing image range radiation estimation method, which can estimate the range radiation of each band at the imaging time according to the self characteristics of the remote sensing image and has the characteristics of simplicity, feasibility and accurate result.
In order to achieve the purpose, the invention adopts the technical scheme that:
a remote sensing image range radiation estimation method comprises the following steps:
(1) searching pixels of the remote sensing image, and forming a pair of shadow pixels and illumination pixels with the same surface coverage type; the remote sensing image comprises images of a plurality of wave bands;
(2) according to the radiation energy received by each pixel in the multiple pairs of pixels in each wave band obtained in the step (1), performing statistical regression by using the following formula for each wave band, and estimating the path radiation of each wave band at the imaging moment of the remote sensing image:
R shadow ＝V·R sunlit +(1-V)·R path
wherein,
in the formula, R shadow For the radiation energy received by the shadow pixels, R sunlit For the radiation energy received by the illuminating pixel, R path In order to be the range radiation,
for downward scattering of energy, R, from the atmosphere direct Is direct energy downward from the atmosphere.
Further, the specific mode of the step (1) is as follows:
(101) dividing the remote sensing image into homogeneous patches by using an image dividing method, wherein each patch comprises a plurality of pixels, extracting shadows by using average brightness of the patches, contrast with adjacent bright patches and patch size characteristics as classification characteristics, and using all pixels on two sides of a shadow boundary and within two pixels of the shadow boundary horizontal distance as penumbra pixels;
(102) the shadow pixels and the illumination pixels which are respectively positioned at the two sides of the shadow boundary and are positioned in the same horizontal direction and are separated by four half-image pixels form image element pairs, and the spectral correlation coefficient and the brightness ratio of each image element pair are calculated; sorting the spectral correlation coefficients and the brightness ratios of all the pixel pairs from large to small, and taking the pixel pairs with the spectral correlation coefficient being in the first 50% and the brightness ratio being in the last 50% as the shadow pixel and the illumination pixel pairs with the same ground surface coverage type.
Compared with the background technology, the invention has the following advantages:
1. according to the modeling of the range radiation in the shadow imaging model, the regression relationship between the shadow pixels and the illumination pixels with the same earth surface coverage is used for solving the range radiation of each wave band, and the method is simple and easy to implement and convenient to realize.
2. The method can accurately evaluate the range radiation value of each wave band under the condition that a dark target is absent on the remote sensing image.
Drawings
FIG. 1 is a flow chart of a method for range radiance estimation in an embodiment of the present invention.
FIG. 2 is a remote sensing image map used in an embodiment of the present invention.
FIG. 3 is a diagram of shadow region extraction results according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of the principle of searching for illumination and shadow pixels with the same surface coverage in an embodiment of the present invention.
FIG. 5 is an illumination pel that has the same ground cover type as a shadow pel, as searched for in an embodiment of the invention.
FIG. 6 is a shadow pel that has the same ground cover type as the lighting pel searched for in an embodiment of the invention.
FIG. 7 shows the regression result of the blue band illumination pel and the shadow pel in the embodiment of the invention.
FIG. 8 is a regression result of green band illumination pixels and shadow pixels in an embodiment of the invention.
FIG. 9 shows the regression result of the red band illumination pixels and the shadow pixels in the embodiment of the present invention.
FIG. 10 shows the regression result of the near infrared band illumination pixels and the shadow pixels in the embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention are described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
As shown in fig. 1, a remote sensing image range radiation estimation method includes the following steps:
(1) and (5) image segmentation. And (3) segmenting the remote sensing image into homogeneous patches by using an object-oriented segmentation algorithm, and selecting an optimal segmentation scale according to the visual effect. The remote sensing image includes imaging images of a plurality of wave bands, which is common knowledge and is not described in detail.
(2) And extracting a shadow area. And according to the divided patches, classifying by using the average brightness of the wave bands, the brightness difference with the adjacent patches and the patch area as features, and extracting shadow areas in the image.
(3) And searching the shadow pixels and the illumination pixels which cover the same with the earth surface of the shadow pixels. Because the shadow extraction inevitably has errors, two pixels inside and outside the shadow extraction boundary are assumed to be half-pel pixels. As shown in fig. 4, the outermost pixels in the shadow area are taken as the boundary, and it is assumed that the pixels in the 2-pixel buffer areas around the boundary are half-pixels, and the pixels outside the 2-pixel buffer areas are shadow pixels or illumination pixels; the method comprises the following steps that an illumination pixel and a shadow pixel at two ends of four continuous half-image pixels in the horizontal direction are taken as a pair (such as an A pixel and a B pixel in figure 4), namely, the illumination pixel and the shadow pixel in each pair of pixels are respectively positioned at two sides of a shadow boundary, and the two pixels are separated by four half-image pixels; respectively calculating the spectral correlation coefficient and the brightness ratio of the shadow pixel and the corresponding illumination pixel, and then respectively sorting the spectral correlation coefficient and the brightness ratio from large to small, wherein the assumption is that the spectral correlation coefficient is in the first 50 percent and the pixel with the brightness ratio in the last 50 percent is the shadow pixel and the illumination pixel with the same ground surface coverage.
(4) And estimating path radiation based on regression of shadow pixels and illumination pixels. According to the plurality of pairs of shadow pixels and illumination pixels with the same earth surface coverage searched in the step (3), calculating the radiation energy received by each pixel searched under the wave band aiming at each wave band, and performing statistical regression by using the following formula to obtain the range radiation of each wave band at the moment of acquiring the remote sensing image:
R shadow ＝V·R sunlit +(1-V)·R path ，
wherein,
in the formula, R shadow For the radiation energy received by the shadow pixels, R sunlit For the radiation energy received by the illuminating pixel, R path In order to be the range radiation,
for downward scattering of energy, R, from the atmosphere direct Is direct energy downward from the atmosphere.
The principle of the method is as follows:
because the shadow image element only receives atmospheric scattered light, and direct light is shielded by an object, the radiation energy received by the shadow image element of the sensor layer is as follows:
wherein R is shadow For the radiation energy received by the shadow pixels,
for atmospheric down-scattered energy, p 1 Reflectivity of a certain surface coverage type, τ ↑ For upward atmospheric transmittance, R path Is the range radiation. The radiation energy received by the sensor layer illumination pixel is as follows:
wherein R is sunlit For the radiation energy received by the illuminating pixel, R direct For direct energy downward of the atmosphere, p 2 Is the reflectivity of a certain surface coverage type. Assuming that the earth surface coverage types of the shadow pixel and the illumination pixel are the same, the following steps are provided:
ρ 1 ＝ρ 2 (3)
order to
The radiation energy of the shadow pixel and the radiation energy of the illumination pixel have the following relationship:
R shadow ＝V·R sunlit +(1-V)·R path (4)
in order to verify the effectiveness of the method, shadow extraction is carried out on the graph 2 to obtain a graph 3, then an illumination pixel graph 5 and a shadow pixel graph 6 with the same surface coverage type are extracted by using the graph 3, and finally statistical regression is carried out according to DN values (namely pixel brightness values of remote sensing images) of all wave bands to obtain range radiation (shown in the graph 7-10) of all the wave bands: the path radiation DN value of the blue light wave band is 298.24, the path radiation DN value of the green light wave band is 357.19, the path radiation DN value of the red light wave band is 154.14, and the path radiation DN value of the near infrared wave band is 61.54.
The method utilizes the radiation energy difference of the shadow area and the illumination area on the remote sensing image to image, and uses a regression method to calculate the path radiation of each wave band. Specifically, firstly, extracting shadow pixels on a remote sensing image; then, a shadow pixel and an illumination pixel with the same earth surface coverage and the distance of 4 are searched in the horizontal direction by utilizing the spectral included angle and the spectral Euclidean distance measurement; and finally, obtaining the range radiation value of each wave band by using a statistical regression method. The method does not need to assume that a dark target with the reflectivity close to 0 exists on the remote sensing image, and is suitable for radiation estimation of all shaded remote sensing image paths.
In a word, the method realizes the path radiation estimation of the remote sensing image, and estimates the path radiation value of each wave band by adopting the statistical regression relationship between the shadow region and the illumination region. The method can still well estimate the range radiation value under the condition that no dark target exists on the remote sensing image.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments and that all inventions utilizing the concepts of the present invention are protected.
Claims (2)
1. A remote sensing image range radiation estimation method is characterized by comprising the following steps:
(1) searching pixels of the remote sensing image, and forming a pair of shadow pixels and illumination pixels with the same surface coverage type; the remote sensing image comprises images of a plurality of wave bands;
(2) according to the radiation energy received by each pixel in the multiple pairs of pixels in each wave band obtained in the step (1), performing statistical regression by using the following formula for each wave band to estimate the path radiation of each wave band at the imaging moment of the remote sensing image:
R shadow ＝V·R sunlit +(1-V)·R path
wherein,
in the formula, R shadow Radiant energy received for shadow pixelsAmount, R sunlit For the radiation energy received by the illuminating pixel, R path In order to achieve the range radiation,
for downward scattering of energy, R, from the atmosphere direct Is direct energy downward from the atmosphere.
2. The remote sensing image range radiation estimation method according to claim 1, wherein the specific mode of the step (1) is as follows:
(101) dividing the remote sensing image into homogeneous patches by using an image dividing method, wherein each patch comprises a plurality of pixels, extracting shadows by using average brightness of the patches, contrast with adjacent bright patches and patch size characteristics as classification characteristics, and using all pixels on two sides of a shadow boundary and within two pixels of the shadow boundary horizontal distance as penumbra pixels;
(102) the shadow pixels and the illumination pixels which are respectively positioned at the two sides of the shadow boundary and are positioned in the same horizontal direction and are separated by four half-image pixels form image element pairs, and the spectral correlation coefficient and the brightness ratio of each image element pair are calculated; sorting the spectral correlation coefficients and the brightness ratios of all the pixel pairs from large to small, and taking the pixel pairs with the spectral correlation coefficient being in the first 50% and the brightness ratio being in the last 50% as the shadow pixel and the illumination pixel pairs with the same ground surface coverage type.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010842808.XA CN111947773B (en) | 2020-08-20 | 2020-08-20 | Remote sensing image path radiation estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010842808.XA CN111947773B (en) | 2020-08-20 | 2020-08-20 | Remote sensing image path radiation estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111947773A CN111947773A (en) | 2020-11-17 |
| CN111947773B true CN111947773B (en) | 2022-08-02 |
Family
ID=73358601
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010842808.XA Active CN111947773B (en) | 2020-08-20 | 2020-08-20 | Remote sensing image path radiation estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111947773B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101539998A (en) * | 2009-04-29 | 2009-09-23 | 中国地质科学院矿产资源研究所 | Alteration remote sensing abnormity extraction method and system |
| CN104049256A (en) * | 2014-05-29 | 2014-09-17 | 中国科学院遥感与数字地球研究所 | Physical method for computing atmospheric path radiance of satellite remote sensing images through picture elements one by one |
| CN108107002A (en) * | 2017-11-23 | 2018-06-01 | 中国科学院合肥物质科学研究院 | The in-orbit absolute radiation calibration method of Radiance transfer calculation is simplified based on multiple level target |
| CN108120510A (en) * | 2017-12-08 | 2018-06-05 | 中国科学院合肥物质科学研究院 | A kind of in-orbit absolute radiation calibration method of optical sensor based on reflection mirror array |
| CN108198178A (en) * | 2018-01-02 | 2018-06-22 | 石家庄学院 | The determining method and apparatus of atmospheric path radiation value |
-
2020
- 2020-08-20 CN CN202010842808.XA patent/CN111947773B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101539998A (en) * | 2009-04-29 | 2009-09-23 | 中国地质科学院矿产资源研究所 | Alteration remote sensing abnormity extraction method and system |
| CN104049256A (en) * | 2014-05-29 | 2014-09-17 | 中国科学院遥感与数字地球研究所 | Physical method for computing atmospheric path radiance of satellite remote sensing images through picture elements one by one |
| CN108107002A (en) * | 2017-11-23 | 2018-06-01 | 中国科学院合肥物质科学研究院 | The in-orbit absolute radiation calibration method of Radiance transfer calculation is simplified based on multiple level target |
| CN108120510A (en) * | 2017-12-08 | 2018-06-05 | 中国科学院合肥物质科学研究院 | A kind of in-orbit absolute radiation calibration method of optical sensor based on reflection mirror array |
| CN108198178A (en) * | 2018-01-02 | 2018-06-22 | 石家庄学院 | The determining method and apparatus of atmospheric path radiation value |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111947773A (en) | 2020-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108596103A (en) | High resolution ratio satellite remote-sensing image building extracting method based on optimal spectrum Index selection | |
| CN109613513B (en) | Optical remote sensing potential landslide automatic identification method considering InSAR deformation factor | |
| Kirk et al. | Estimation of leaf area index in cereal crops using red–green images | |
| US8705887B2 (en) | Method and apparatus for filling in or replacing image pixel data | |
| WO2020015326A1 (en) | Remote sensing image cloud shadow detection method supported by earth surface type data | |
| US9418411B2 (en) | System and method for sun glint correction of split focal plane visible and near infrared imagery | |
| US20200250840A1 (en) | Shadow detection method and system for surveillance video image, and shadow removing method | |
| CN111553922B (en) | Automatic cloud detection method for satellite remote sensing image | |
| CN109376600A (en) | Multi-spectrum remote sensing image comprehensive characteristics cloud detection method of optic and device | |
| CN114005042A (en) | Remote sensing image urban building extraction method based on shadow compensation and U-net | |
| CN104966298A (en) | Night low-cloud heavy-mist monitoring method based on low-light cloud image data | |
| US20210286970A1 (en) | Cloud detection method based on landsat 8 snow-containing image | |
| CN107103295B (en) | Optical remote sensing image cloud detection method | |
| CN109858394A (en) | A kind of remote sensing images water area extracting method based on conspicuousness detection | |
| CN104778668A (en) | Optical remote sensing image thin cloud removing method based on visible light band spectrum statistical characteristics | |
| CN111489299A (en) | Defogging method for multispectral remote sensing satellite image | |
| CN109741337B (en) | Region merging watershed color remote sensing image segmentation method based on Lab color space | |
| CN111947773B (en) | Remote sensing image path radiation estimation method | |
| CN103900498B (en) | A kind of cotton field automatic detection method of the growth of cereal crop seedlings and detection device thereof | |
| CN111667432B (en) | Remote sensing image shadow removing method based on physical model | |
| CN109033984B (en) | Night fog rapid automatic detection method | |
| CN114596234A (en) | NDVI terrain shadow effect correction method for complex mountainous region | |
| Nagare et al. | Cloud Shadow Removal Based on Cloud Transmittance Estimation | |
| CN116630349B (en) | Straw returning area rapid segmentation method based on high-resolution remote sensing image | |
| Ma et al. | Shadow removal in remote sensing images using features sample matting |
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 |