CN103903225B - A kind of Technique for Hyper-spectral Images Classification for dolomite information retrieval - Google Patents
A kind of Technique for Hyper-spectral Images Classification for dolomite information retrieval Download PDFInfo
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- CN103903225B CN103903225B CN201210573014.3A CN201210573014A CN103903225B CN 103903225 B CN103903225 B CN 103903225B CN 201210573014 A CN201210573014 A CN 201210573014A CN 103903225 B CN103903225 B CN 103903225B
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Abstract
The present invention relates to a kind of Technique for Hyper-spectral Images Classification for dolomite information retrieval, retain the obvious wave band of spectral signature of dolomite, extract the wave band image at 1820nm, 1985nm, 2015nm, 2195nm, 2315nm, 2380nm, 2420nm, carry out a series of judgement and calculating, calculate the Abundances of zones of different dolomite in image capturing range.This method can remove the unconspicuous wave band of other features, thus the spectral signature of dolomite is highlighted in the process of information retrieval, reduce other atural objects or effect of noise, decrease the data volume of process, and can reach to realize the purpose of dolomite information retrieval with less manual operation by IDL program, improve the accuracy and speed of dolomite information retrieval.
Description
Technical field
The present invention relates to a kind of Technique for Hyper-spectral Images Classification for dolomite information retrieval, particularly relate to
And a kind of reduce other atural objects or effect of noise, manual steps and process data volume for white clouds
The Technique for Hyper-spectral Images Classification of stone information retrieval.
Background technology
The dolomite information extracting method of current target in hyperspectral remotely sensed image be mainly spectrum all band coupling or
Being the Spectral matching of part wave band continuously, specific algorithm has spectral modeling, mixing demodulation filtering etc., due to ground
The material of table forms seldom by single mineral composition, and these methods are in the process of dolomite information retrieval
Easily by other ground-object spectrums or effect of noise, extract precision of information relatively low.Secondly existing spectrum
Extracting method manual steps is many, adds artificial error in judgement.3rd is that high-spectral data wave band is many,
Data volume is big, and it is long that existing method processes the time, reduces speed and application scale that data process.Cause
How this, reduce other atural objects or effect of noise, manual operation during dolomite information retrieval
Step and process data volume, become one of forward position of current target in hyperspectral remotely sensed image process.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of other atural objects or effect of noise, artificial of reducing
Operating procedure and the Technique for Hyper-spectral Images Classification for dolomite information retrieval of process data volume.
For solving above-mentioned technical problem, the present invention is a kind of at the Hyperspectral imaging of dolomite information retrieval
Reason method, comprises the following steps:
The first step, obtains Hyperspectral imaging；Hyperspectral imaging is carried out pretreatment, carries out atmospheric correction,
Obtain the image data of ground surface reflectance；
Second step, carries out resampling to the image data of ground surface reflectance, extract wave band 1820nm,
The image of 1985nm, 2015nm, 2195nm, 2315nm, 2380nm, 2420nm；
3rd step, when the picture that the pixel value of 1820nm wave band image is corresponding more than 1985nm wave band image
Unit's value, it is judged that this pixel meets condition one；Obtain 1820nm wave band image and deduct 1985nm wave band image
Result image H1；
4th step, when the picture that the pixel value of 1985nm wave band image is corresponding less than 2015nm wave band image
Unit's value, it is judged that this pixel meets condition two；Obtain 2015nm wave band image and deduct 1985nm wave band image
Result image H2；
5th step, when the picture that the pixel value of 2195nm wave band image is corresponding more than 2315nm wave band image
Unit's value, it is judged that this pixel meets condition three；Obtain 2195nm wave band image and deduct 2315nm wave band image
Result image H3；
6th step, when the pixel value of 2315nm wave band image is corresponding less than 2380nm wave band image
Pixel value, it is judged that this pixel meets condition four；Obtain 2380nm wave band image and deduct 2315nm wave band shadow
The result image H4 of picture；
7th step, when the pixel value of 2380nm wave band image is corresponding more than 2420nm wave band image
Pixel value, it is judged that this pixel meets condition five；Obtain 2380nm wave band image and deduct 2420nm wave band
The result image H5 of image；
8th step, selects to meet the pixel of condition one to condition five, it is thus achieved that in the range of above-mentioned pixel simultaneously
H1, H2, H3, H4, H5 be added and H.
The present invention can reduce the data volume of process, and the wave band quantity of SASI is 101 wave bands, this method
Being served only for 7 wave bands, data volume decreases 94%, and owing to being the automatic onestep extraction of computer, subtracts
The operating procedures such as the selection having lacked principal component transform, end member wave spectrum, arithmetic speed can improve 13 times with
On.The wave band little to information retrieval relation owing to eliminating major part, reduces other materials or noise pair
The interference of its spectrum, improves the precision of information retrieval.To dolomite information in airborne-remote sensing
Rapid extraction has preferable function and significance.
Detailed description of the invention
The present invention, by Hyperspectral imaging resampling, extracts specific band, carries out a series of judgement and meter
Calculate, calculate the Abundances of zones of different dolomite in image capturing range.
Specifically include following steps:
The first step, obtains Hyperspectral imaging；Hyperspectral imaging is carried out pretreatment, carries out atmospheric correction,
Obtain the image data of ground surface reflectance；
Second step, carries out resampling to the image data of ground surface reflectance, extract wave band 1820nm,
The image of 1985nm, 2015nm, 2195nm, 2315nm, 2380nm, 2420nm；
3rd step, when the picture that the pixel value of 1820nm wave band image is corresponding more than 1985nm wave band image
Unit's value, it is judged that this pixel meets condition one；Obtain 1820nm wave band image and deduct 1985nm wave band image
Result image H1；
4th step, when the picture that the pixel value of 1985nm wave band image is corresponding less than 2015nm wave band image
Unit's value, it is judged that this pixel meets condition two；Obtain 2015nm wave band image and deduct 1985nm wave band image
Result image H2；
5th step, when the picture that the pixel value of 2195nm wave band image is corresponding more than 2315nm wave band image
Unit's value, it is judged that this pixel meets condition three；Obtain 2195nm wave band image and deduct 2315nm wave band image
Result image H3；
6th step, when the pixel value of 2315nm wave band image is corresponding less than 2380nm wave band image
Pixel value, it is judged that this pixel meets condition four；Obtain 2380nm wave band image and deduct 2315nm wave band shadow
The result image H4 of picture；
7th step, when the pixel value of 2380nm wave band image is corresponding more than 2420nm wave band image
Pixel value, it is judged that this pixel meets condition five；Obtain 2380nm wave band image and deduct 2420nm wave band
The result image H5 of image；
8th step, selects to meet the pixel of condition one to condition five, it is thus achieved that in the range of above-mentioned pixel simultaneously
H1, H2, H3, H4, H5 be added and H.
The value of H represents the Abundances of pixel dolomite, and H-number is the biggest, represents the abundance of dolomite in pixel
The biggest, i.e. content is the highest.
Claims (1)
1., for a Technique for Hyper-spectral Images Classification for dolomite information retrieval, comprise the following steps:
The first step, obtains Hyperspectral imaging；Hyperspectral imaging is carried out pretreatment, carries out atmospheric correction, obtain the image data of ground surface reflectance；
Second step, carries out resampling to the image data of ground surface reflectance, extracts the wave band image at 1820nm, 1985nm, 2015nm, 2195nm, 2315nm, 2380nm, 2420nm；
3rd step, when the pixel value that the pixel value of 1820nm wave band image is corresponding more than 1985nm wave band image, it is judged that this pixel meets condition one；Obtain 1820nm wave band image and deduct the result image H1 of 1985nm wave band image；
4th step, when the pixel value that the pixel value of 1985nm wave band image is corresponding less than 2015nm wave band image, it is judged that this pixel meets condition two；Obtain 2015nm wave band image and deduct the result image H2 of 1985nm wave band image；
5th step, when the pixel value that the pixel value of 2195nm wave band image is corresponding more than 2315nm wave band image, it is judged that this pixel meets condition three；Obtain 2195nm wave band image and deduct the result image H3 of 2315nm wave band image；
6th step, when the pixel value that the pixel value of 2315nm wave band image is corresponding less than 2380nm wave band image, it is judged that this pixel meets condition four；Obtain 2380nm wave band image and deduct the result image H4 of 2315nm wave band image；
7th step, when the pixel value that the pixel value of 2380nm wave band image is corresponding more than 2420nm wave band image, it is judged that this pixel meets condition five；Obtain 2380nm wave band image and deduct the result image H5 of 2420nm wave band image；
8th step, selects to meet the pixel of condition one to condition five simultaneously, it is thus achieved that H1, H2, H3, H4, the H5 in the range of above-mentioned pixel is added and H.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105787914A (en) * | 2014-12-22 | 2016-07-20 | 核工业北京地质研究院 | Hyperspectral image processing method for alunite information extraction |
| CN105787915A (en) * | 2014-12-22 | 2016-07-20 | 核工业北京地质研究院 | Hyperspectral image processing method for extracting information of jarosite |
| CN105787483A (en) * | 2014-12-22 | 2016-07-20 | 核工业北京地质研究院 | Hyperspectral image processing method for extracting information of lizardite |
| CN105784602A (en) * | 2014-12-22 | 2016-07-20 | 核工业北京地质研究院 | High spectral image processing method for extracting information of rhodochrosite |
| CN105787916A (en) * | 2014-12-22 | 2016-07-20 | 核工业北京地质研究院 | Hyperspectral image processing method for extracting information of dickite |
| US10235547B2 (en) | 2016-01-26 | 2019-03-19 | Hand Held Products, Inc. | Enhanced matrix symbol error correction method |
| CN111044464A (en) * | 2019-12-19 | 2020-04-21 | 核工业北京地质研究院 | Data processing method suitable for extracting vegetation abundance information |
| CN111157459A (en) * | 2019-12-20 | 2020-05-15 | 核工业北京地质研究院 | Hyperspectral image processing method for mineral information extraction |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1595203A (en) * | 2004-06-29 | 2005-03-16 | 中国国土资源航空物探遥感中心 | Layered lineage identification method for high spectrum mineral |
| CN101887012A (en) * | 2010-06-28 | 2010-11-17 | 中国国土资源航空物探遥感中心 | Spectral reflectance peak decomposition based quantitative inversion method of hyperspectral remote sensing mineral content |
| CN102609711A (en) * | 2012-02-21 | 2012-07-25 | 核工业北京地质研究院 | Information extraction method applicable to hyperspectral image |
| CN102636778A (en) * | 2012-02-21 | 2012-08-15 | 核工业北京地质研究院 | Information extracting method suitable for high-spectrum image |
-
2012
- 2012-12-25 CN CN201210573014.3A patent/CN103903225B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1595203A (en) * | 2004-06-29 | 2005-03-16 | 中国国土资源航空物探遥感中心 | Layered lineage identification method for high spectrum mineral |
| CN101887012A (en) * | 2010-06-28 | 2010-11-17 | 中国国土资源航空物探遥感中心 | Spectral reflectance peak decomposition based quantitative inversion method of hyperspectral remote sensing mineral content |
| CN102609711A (en) * | 2012-02-21 | 2012-07-25 | 核工业北京地质研究院 | Information extraction method applicable to hyperspectral image |
| CN102636778A (en) * | 2012-02-21 | 2012-08-15 | 核工业北京地质研究院 | Information extracting method suitable for high-spectrum image |
Non-Patent Citations (3)
| Title |
|---|
| Spectral reflectance of carbonate minerals in the visible and near infrared(0.35-2.55 microns):calcite,aragonite,and dolomite;Susan J. Gaffey;《American Mineralogist》;19860228;第71卷(第1-2期);第160页右栏第4段、图14,15 * |
| 东昆仑纳赤台地区高光谱遥感岩矿填图研究;郭洪义;《中国优秀硕士学位论文全文数据库 基础科学辑》;20100215(第2期);摘要、第13-15页第3.2节,第18-26页第4.2节,第46页第2段、图2-3 * |
| 新疆阿勒泰地区典型矿物岩石光谱特征;彭光雄 叶震超 等;《新疆阿勒泰地区典型矿物岩石光谱特征》;http://image.sciencenet.cn/olddata/kexue.com.cn/upload/blog/file/2010/11/20101129133811453846.pdf;20101231;第1.2.1-1.2.2,1.4.2节 * |
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