CN112378864A - Airborne hyperspectral soil information retrieval method - Google Patents
Airborne hyperspectral soil information retrieval method Download PDFInfo
- Publication number
- CN112378864A CN112378864A CN202011165167.5A CN202011165167A CN112378864A CN 112378864 A CN112378864 A CN 112378864A CN 202011165167 A CN202011165167 A CN 202011165167A CN 112378864 A CN112378864 A CN 112378864A
- Authority
- CN
- China
- Prior art keywords
- spectrum
- image
- soil
- centroid
- sampling point
- 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.)
- Pending
Links
- 239000002689 soil Substances 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000001228 spectrum Methods 0.000 claims abstract description 135
- 238000002310 reflectometry Methods 0.000 claims abstract description 37
- 238000005527 soil sampling Methods 0.000 claims abstract description 31
- 238000005070 sampling Methods 0.000 claims abstract description 22
- 238000012937 correction Methods 0.000 claims abstract description 15
- 238000005259 measurement Methods 0.000 claims abstract description 13
- 230000003595 spectral effect Effects 0.000 claims abstract description 11
- 206010061274 Malocclusion Diseases 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims abstract description 5
- 230000005855 radiation Effects 0.000 claims abstract description 5
- 238000012216 screening Methods 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 238000007621 cluster analysis Methods 0.000 claims description 4
- 230000000873 masking effect Effects 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 230000004807 localization Effects 0.000 abstract 1
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 12
- 238000011160 research Methods 0.000 description 7
- 229910052757 nitrogen Inorganic materials 0.000 description 6
- 238000013507 mapping Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- BUGBHKTXTAQXES-UHFFFAOYSA-N Selenium Chemical compound [Se] BUGBHKTXTAQXES-UHFFFAOYSA-N 0.000 description 1
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 1
- 229910052804 chromium Inorganic materials 0.000 description 1
- 239000011651 chromium Substances 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000011133 lead Substances 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 239000011669 selenium Substances 0.000 description 1
- 229910052711 selenium Inorganic materials 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000011701 zinc Substances 0.000 description 1
- 229910052725 zinc Inorganic materials 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention belongs to the technical field of geological exploration, and particularly relates to an airborne hyperspectral soil information retrieval method, which comprises the following steps: (1) carrying out geometric correction, radiation correction and atmospheric correction on the airborne spectrum original data to generate airborne hyperspectral reflectivity image data; (2) generating a soil sampling point image spectrum library on the airborne hyperspectral reflectivity image data; (3) screening out an image spectrum set which is closest to a ground measurement spectrum of the sampling point from a soil sampling point image spectrum library; (4) constructing a hyperspectral loam localization information matrix, and calculating a soil information regression equation; (5) carrying out spectrum angle classification on the airborne high spectral reflectivity image data by adopting the screened soil sampling point image spectrum set; generating masked image reflectance data; (6) and calculating the image reflectivity data after the mask according to a regression equation of the soil information to obtain a soil information inversion result.
Description
Technical Field
The invention belongs to the technical field of geological exploration, and particularly relates to an airborne hyperspectral soil information retrieval method.
Background
A large amount of hyperspectral data has become available for observing earth systems from multi-platform sensors, ranging from ground spectrometers on the earth, unmanned aerial vehicle hyperspectral measurement systems to aerial and satellite remote sensing systems on the earth, ranging from a few meters to a few hundred meters. The airborne hyperspectral data are important data sources, and the regional soil geochemical mapping performed by the airborne hyperspectral data has important research significance on soil investigation, soil texture analysis and agriculture. Due to the fact that the scale of the current airborne hyperspectral data is inconsistent with the scale of the ground measured spectrum data and the influence of interference factors such as moisture, atmosphere and the like, the hyperspectral geochemistry mapping accuracy of the regional scale is not high.
Therefore, an airborne hyperspectral soil information inversion method needs to be designed for research, and a hyperspectral soil information quantitative analysis model is realized.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an airborne hyperspectral soil information retrieval method, which is used for solving the technical problem that the hyperspectral geochemistry mapping precision of the regional scale is not high due to the inconsistent scale of airborne hyperspectral data and ground measured spectrum data and the influence of interference factors such as moisture, atmosphere and the like in the prior art.
The technical scheme of the invention is as follows:
an airborne hyperspectral soil information retrieval method comprises the following steps:
the method comprises the following steps: geometric correction, radiation correction and atmospheric correction are carried out on the airborne spectrum original data, water vapor absorption wave bands are removed, all the aerial bands are inlaid, and airborne hyperspectral reflectivity image data are generated;
step two: collecting an average reflectivity spectrum of an image area in a certain range on airborne high-spectral reflectivity image data by taking the coordinates of each soil ground sampling point as a center, and generating a soil sampling point image spectrum library according to the serial number sequence of the ground sampling points;
step three: performing cluster analysis on the soil sampling point image spectrum library in the second step, and screening out an image spectrum set closest to the ground measurement spectrum of the sampling point;
step four: constructing a hyperspectral loam information matrix by the soil sampling point spectrum set and the geochemical analysis results of corresponding sampling points, and calculating a regression equation of soil information by adopting a partial least square method;
step five: carrying out spectrum angle classification on the airborne high spectral reflectivity image data obtained in the first step by adopting the soil sampling point image spectrum set screened out in the third step; according to the classification result, only image pixels representing soil in the image are reserved by using a masking method, and masked image reflectivity data are generated;
step six: and calculating the image reflectivity data after the mask according to the regression equation of the soil information in the step four to obtain the soil information inversion result.
In the second step, the average reflectivity spectrum of the image area in a certain range is collected, and the collection method comprises the following steps: selecting M x M as the number of pixels of the collected image, wherein the initial values of M are respectively 3, 5, 7 and 9; and respectively calculating the spectrum angle errors of the image area average reflectivity spectrum with different M values and the corresponding point ground measurement spectrum, and selecting the average reflectivity spectrum with the minimum spectrum angle error.
The third step further comprises the following substeps:
step 3.1: setting a soil sampling point image spectrum set number K;
step 3.2: randomly selecting k image spectrums from a soil sampling point image spectrum library as a mass center;
step 3.3: calculating the distance between each image spectrum in the image spectrum library and each centroid, and taking the distance between each image spectrum and each centroid as a division principle, namely: when the image spectrum is close to a certain centroid, the set to which the centroid belongs is divided;
step 3.4: after all image spectrums are grouped into sets, k sets exist in total; then re-computing the centroid of each set;
step 3.5: if the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold value, clustering reaches an expected result, and the algorithm is terminated; otherwise, repeating the step 3.3-3.5;
step 3.6: and carrying out spectrum angle operation on the centroid of each set and the ground measurement spectrum of the sampling point, wherein the image spectrum set with the minimum centroid of the spectrum angle is the soil sampling point spectrum set.
In the step 3.3: for each image spectrum in the image spectrum library, calculating the distance between each image spectrum and each centroid, wherein the formula for calculating the distance is shown as the following formula (1):
wherein, ciAs a set of image spectra (c)1，c2，...，ck) (ii) a x is the reflectance value of the image spectrum; implicit layer features, K is the number of sets of clusters; mu.siThe centroid spectral reflectance for the ith set.
In step 3.4, the centroid of each set is recalculated, and the formula is calculated, as shown in the following formula (2):
wherein, ciAs a set of image spectra (c)1，c2，...，ck): x is the reflectance value of the image spectrum; mu.siThe centroid spectral reflectance for the ith set.
The invention has the beneficial effects that:
according to the airborne hyperspectral soil information retrieval method, a great deal of experimental research is carried out on nutrient elements (nitrogen, phosphorus and potassium) and heavy metal elements (copper, lead, zinc, chromium and the like) of soil and beneficial elements (selenium), the distribution and the content of the elements can be effectively extracted, the applicability is strong, and the method has reference significance for quantitative analysis work of other elements.
Detailed Description
The airborne hyperspectral soil information inversion method designed by the invention is further described by combining the specific embodiment and the technical scheme, and comprises the following steps:
the method comprises the following steps: acquiring airborne spectrum original data of a research area, performing geometric correction, radiation correction and atmospheric correction on the airborne spectrum original data, removing water vapor absorption wave bands, completing embedding of all air bands, and generating airborne hyperspectral reflectivity image data;
step two: collecting an average reflectivity spectrum of an image area in a certain range on airborne high-spectral reflectivity image data by taking the coordinates of each soil ground sampling point as a center, and generating a soil sampling point image spectrum library according to the serial number sequence of the ground sampling points;
in the second step, the average reflectivity spectrum of the image area in a certain range is collected, and the collection method comprises the following steps: selecting M x M as the number of pixels of the collected image, wherein the initial values of M are respectively 3, 5, 7 and 9; and respectively calculating the spectrum angle errors of the image area average reflectivity spectrum with different M values and the corresponding point ground measurement spectrum, and selecting the average reflectivity spectrum with the minimum spectrum angle error.
Step three: performing cluster analysis on the soil sampling point image spectrum library in the second step, and screening out an image spectrum set closest to the ground measurement spectrum of the sampling point;
the third step further comprises the following substeps:
step 3.1: setting a soil sampling point image spectrum set number K;
step 3.2: randomly selecting k image spectrums from a soil sampling point image spectrum library as a mass center;
step 3.3: calculating the distance between each image spectrum in the image spectrum library and each centroid, and taking the distance between each image spectrum and each centroid as a division principle, namely: when the image spectrum is close to a certain centroid, the set to which the centroid belongs is divided;
in the step 3.3: for each image spectrum in the image spectrum library, calculating the distance between each image spectrum and each centroid according to the following formula (1):
wherein, ciAs a set of image spectra (c)1，c2，...，ck) (ii) a x is the reflectance value of the image spectrum; implicit layer features, K is the number of sets of clusters; mu.siThe centroid spectral reflectance for the ith set.
Step 3.4: after all image spectrums are grouped into sets, k sets exist in total; then, the centroid of each set is recalculated, and the calculation formula is as shown in the following formula (2):
wherein, ciAs a set of image spectra (c)1，c2，...，ck) (ii) a x is the reflectance value of the image spectrum; mu.siThe centroid spectral reflectance for the ith set.
Step 3.5: if the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold value, clustering reaches an expected result, and the algorithm is terminated; otherwise, repeating the step 3.3-3.5;
step 3.6: and carrying out spectrum angle operation on the centroid of each set and the ground measurement spectrum of the sampling point, wherein the image spectrum set with the minimum centroid of the spectrum angle is the soil sampling point spectrum set.
Step four: constructing a hyperspectral loam information matrix by the soil sampling point spectrum set and the geochemical analysis results of corresponding sampling points, and calculating a regression equation of soil information by adopting a partial least square method;
step five: carrying out spectrum angle classification on the airborne high spectral reflectivity image data of the research area in the first step by adopting the soil sampling point image spectrum set screened out in the third step; according to the classification result, only image pixels representing soil in the image are reserved by using a masking method, and masked image reflectivity data are generated;
step six: and according to the regression equation of partial least square calculation of the soil information, calculating the image reflectivity data after the mask according to the regression equation of the soil information in the step four, and obtaining the soil information inversion result.
The specific embodiment example:
according to the method, the airborne hyperspectral data SASI is selected to perform content inversion on the total nitrogen of the soil, and other elements can refer to the method.
(1) And selecting a Qixing agricultural field area of black soil of Sanjiang in northeast China for testing, acquiring airborne SASI (geosynthetic inertial navigation System) hyperspectral data of the bare soil, and performing geometric correction, radiation correction and atmospheric correction on the data. The water vapor absorption wave band is removed, the reflectivity image data of the research area is generated, the area is 1500 square kilometers, the spectral interval is 950nm-2450nm, and the total number of 85 effective wave bands are obtained. The spatial resolution is 2.5 m.
(2) The coordinates of the 200 sample points on the surface and the geochemical analysis data are collected. Collecting an average reflectivity spectrum of a 3 x 3 image area on airborne SASI (sampled satellite System) hyperspectral reflectivity image data by taking the coordinates of each soil ground sampling point as a center, and generating a soil sampling point image spectrum library according to the numbering sequence of the ground sampling points;
(3) performing cluster analysis on the soil sampling point image spectrum library, and screening out an image spectrum set closest to a ground measurement spectrum of the sampling point: firstly, setting a soil sampling point image spectrum set number 3; secondly, randomly selecting 3 image spectrums from a soil sampling point image spectrum library as a mass center; then calculating the distance between each image spectrum in the image spectrum library and each centroid, dividing 3 image spectrum sets, and then recalculating the centroid of each set; and finally, setting the maximum iteration frequency to be 50 times, carrying out spectrum angle operation on the mass center of each set and the ground measurement spectrum of the sampling point, and selecting the image spectrum set with the minimum mass center of the spectrum angle as the soil sampling point spectrum set, wherein the number of the sampling points of the set is 163.
(4) Constructing a hyperspectral soil geology information matrix by using the selected soil sampling point spectrum set and the geochemical analysis results of the corresponding sampling points, and calculating a regression equation of soil total nitrogen by adopting a partial least square method:
Y＝7.86534*X1-7.645558*X2-10.13127*X3+40.17681*X4-0.02235985*X5-31.57936*X6-4.228151*X7+2.09692
wherein Y is the total nitrogen content; x1Image reflectance value at a center wavelength of 1332.5 nm; x2An image reflectance value of center wavelength 1512.5 nm; x3Image reflectance value at a center wavelength of 1662.5 nm; x4An image reflectance value of center wavelength 1722.5 nm; x5An image reflectance value of center wavelength 2067.5 nm; x4An image reflectance value of center wavelength 2217.5 nm; x4The image reflectance value is at a center wavelength of 2307.5 nm.
(5) And (4) adopting the screened soil sampling point image spectrum set as a sample, and carrying out spectrum angle classification on the airborne high-spectrum reflectivity image data of the research area. And according to the classification result, only image pixels representing soil in the image are reserved by using a masking method, and the masked image reflectivity data is generated.
(6) And (4) according to the regression equation calculated by partial least squares of total nitrogen calculated in the step (4), performing band operation on the masked image reflectivity data to obtain the soil total nitrogen content inversion result.
While the embodiments of the present invention have been described in detail, the present invention is not limited to the above-described examples, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (5)
1. An airborne hyperspectral soil information retrieval method is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: geometric correction, radiation correction and atmospheric correction are carried out on the airborne spectrum original data, water vapor absorption wave bands are removed, all the aerial bands are inlaid, and airborne hyperspectral reflectivity image data are generated;
step two: collecting an average reflectivity spectrum of an image area in a certain range on airborne high-spectral reflectivity image data by taking the coordinates of each soil ground sampling point as a center, and generating a soil sampling point image spectrum library according to the serial number sequence of the ground sampling points;
step three: performing cluster analysis on the soil sampling point image spectrum library in the second step, and screening out an image spectrum set closest to the ground measurement spectrum of the sampling point;
step four: constructing a hyperspectral loam information matrix by the soil sampling point spectrum set and the geochemical analysis results of corresponding sampling points, and calculating a regression equation of soil information by adopting a partial least square method;
step five: carrying out spectrum angle classification on the airborne high spectral reflectivity image data obtained in the first step by adopting the soil sampling point image spectrum set screened out in the third step; according to the classification result, only image pixels representing soil in the image are reserved by using a masking method, and masked image reflectivity data are generated;
step six: and calculating the image reflectivity data after the mask according to the regression equation of the soil information in the step four to obtain the soil information inversion result.
2. The airborne hyperspectral soil information inversion method according to claim 1, characterized by comprising the following steps: in the second step, the average reflectivity spectrum of the image area in a certain range is collected, and the collection method comprises the following steps: selecting M x M as the number of pixels of the collected image, wherein the initial values of M are respectively 3, 5, 7 and 9; and respectively calculating the spectrum angle errors of the image area average reflectivity spectrum with different M values and the corresponding point ground measurement spectrum, and selecting the average reflectivity spectrum with the minimum spectrum angle error.
3. The airborne hyperspectral soil information inversion method according to claim 1, characterized by comprising the following steps: the third step further comprises the following substeps:
step 3.1: setting a soil sampling point image spectrum set number K;
step 3.2: randomly selecting k image spectrums from a soil sampling point image spectrum library as a mass center;
step 3.3: calculating the distance between each image spectrum in the image spectrum library and each centroid, and taking the distance between each image spectrum and each centroid as a division principle, namely: when the image spectrum is close to a certain centroid, the set to which the centroid belongs is divided;
step 3.4: after all image spectrums are grouped into sets, k sets exist in total; then re-computing the centroid of each set;
step 3.5: if the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold value, clustering reaches an expected result, and the algorithm is terminated; otherwise, repeating the step 3.3-3.5;
step 3.6: and carrying out spectrum angle operation on the centroid of each set and the ground measurement spectrum of the sampling point, wherein the image spectrum set with the minimum centroid of the spectrum angle is the soil sampling point spectrum set.
4. The airborne hyperspectral soil information inversion method according to claim 3, characterized by comprising the following steps: in the step 3.3: for each image spectrum in the image spectrum library, calculating the distance between each image spectrum and each centroid, wherein the formula for calculating the distance is shown as the following formula (1):
wherein, ciAs a set of image spectra (c)1，c2，...，ck) (ii) a x is the reflectance value of the image spectrum; implicit layer features, K is the number of sets of clusters; mu.siThe centroid spectral reflectance for the ith set.
5. The airborne hyperspectral soil information inversion method according to claim 4, characterized by comprising the following steps: in said step 3.4, the centroid of each set is recalculated, and the formula is calculated, as shown in the following formula (2):
wherein, ciAs a set of image spectra (c)1，c2，...，ck) (ii) a x is the reflectance value of the image spectrum; mu.siThe centroid spectral reflectance for the ith set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011165167.5A CN112378864A (en) | 2020-10-27 | 2020-10-27 | Airborne hyperspectral soil information retrieval method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011165167.5A CN112378864A (en) | 2020-10-27 | 2020-10-27 | Airborne hyperspectral soil information retrieval method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112378864A true CN112378864A (en) | 2021-02-19 |
Family
ID=74577653
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011165167.5A Pending CN112378864A (en) | 2020-10-27 | 2020-10-27 | Airborne hyperspectral soil information retrieval method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112378864A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113791040A (en) * | 2021-07-20 | 2021-12-14 | 广州华清环境监测有限公司 | Soil heavy metal detection method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040153284A1 (en) * | 2003-01-31 | 2004-08-05 | Bernstein Lawrence S. | Method for performing automated in-scene based atmospheric compensation for multi-and hyperspectral imaging sensors in the solar reflective spectral region |
| CN104359847A (en) * | 2014-12-08 | 2015-02-18 | 中国科学院遥感与数字地球研究所 | Method and device for acquiring centroid set used for representing typical water category |
| CN205520941U (en) * | 2016-04-26 | 2016-08-31 | 四川建筑职业技术学院 | Metallographic specimen grinds anchor clamps |
| CN108663330A (en) * | 2018-04-19 | 2018-10-16 | 中国国土资源航空物探遥感中心 | A kind of vegetation-covered area soil copper inversion method based on blade measured spectra |
| CN110376138A (en) * | 2019-08-05 | 2019-10-25 | 北京绿土科技有限公司 | Land quality monitoring method based on Airborne Hyperspectral |
| CN110376139A (en) * | 2019-08-05 | 2019-10-25 | 北京绿土科技有限公司 | Soil organic matter content quantitative inversion method based on ground high-spectrum |
-
2020
- 2020-10-27 CN CN202011165167.5A patent/CN112378864A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040153284A1 (en) * | 2003-01-31 | 2004-08-05 | Bernstein Lawrence S. | Method for performing automated in-scene based atmospheric compensation for multi-and hyperspectral imaging sensors in the solar reflective spectral region |
| CN104359847A (en) * | 2014-12-08 | 2015-02-18 | 中国科学院遥感与数字地球研究所 | Method and device for acquiring centroid set used for representing typical water category |
| CN205520941U (en) * | 2016-04-26 | 2016-08-31 | 四川建筑职业技术学院 | Metallographic specimen grinds anchor clamps |
| CN108663330A (en) * | 2018-04-19 | 2018-10-16 | 中国国土资源航空物探遥感中心 | A kind of vegetation-covered area soil copper inversion method based on blade measured spectra |
| CN110376138A (en) * | 2019-08-05 | 2019-10-25 | 北京绿土科技有限公司 | Land quality monitoring method based on Airborne Hyperspectral |
| CN110376139A (en) * | 2019-08-05 | 2019-10-25 | 北京绿土科技有限公司 | Soil organic matter content quantitative inversion method based on ground high-spectrum |
Non-Patent Citations (1)
| Title |
|---|
| 汪大明;秦凯;李志忠;赵英俊;陈伟涛;甘义群;: "基于航空高光谱遥感数据的黑土地有机质含量反演:以黑龙江省建三江地区为例", 《地球科学》, no. 6, 6 June 2018 (2018-06-06), pages 372 - 382 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113791040A (en) * | 2021-07-20 | 2021-12-14 | 广州华清环境监测有限公司 | Soil heavy metal detection method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2023087630A1 (en) | Method for estimating soil salinity of straw residue farmland by using remote sensing construction index | |
| Schlerf et al. | Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy | |
| Hill et al. | Monitoring 20 years of increased grazing impact on the Greek island of Crete with earth observation satellites | |
| Zarco‐Tejada et al. | Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery | |
| CN110376138B (en) | Land quality monitoring method based on aviation hyperspectrum | |
| US5214281A (en) | Method for locating sub-terranean geological aggregate deposits | |
| CN109492563A (en) | A kind of tree species classification method based on unmanned plane Hyperspectral imaging and LiDAR point cloud | |
| Rogge et al. | Mapping of NiCu–PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada | |
| Fang | Canopy clumping index (CI): A review of methods, characteristics, and applications | |
| CN103383348A (en) | Method for extracting altered mineral at vegetation-covered areas by hyperspectral remote sensing | |
| Shukla et al. | An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines | |
| CN110388986B (en) | Land surface temperature inversion method based on TASI data | |
| CN114460013B (en) | Coastal wetland vegetation overground biomass GAN model self-learning remote sensing inversion method | |
| CN115235997A (en) | Soil texture inversion method based on satellite hyperspectral image | |
| CN113866102A (en) | Soil health investigation monitoring method based on spectrum | |
| CN114386497A (en) | Aviation hyperspectral and gamma spectrum data fusion method oriented to uranium mineralization structure | |
| Agar et al. | Remote sensing for mineral exploration–A decade perspective 1997-2007 | |
| CN112378864A (en) | Airborne hyperspectral soil information retrieval method | |
| Jiang et al. | Estimation of rock copper content based on fractional-order derivative and visible near-infrared–shortwave infrared spectroscopy | |
| Khudhur et al. | Comparison of the accuracies of different spectral indices for mapping the vegetation covers in Al-Hawija district, Iraq | |
| CN114241331A (en) | Wetland reed aboveground biomass remote sensing modeling method taking UAV as ground and Sentinel-2 intermediary | |
| Hook et al. | An evaluation of short-wave-infrared (SWIR) data from the AVIRIS and GEOSCAN instruments for mineralogical mapping at Cuprite, Nevada | |
| Gaddis et al. | Decomposition of AVIRIS spectra: extraction of surface-reflectance, atmospheric, and instrumental components | |
| Kawishwar | Atmospheric correction models for retrievals of calibrated spectral profiles from hyperion EO-1 data | |
| CN115128013A (en) | Soil organic matter content space prediction evaluation method based on partition algorithm |
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 |