Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix
Identification of spectrally similar materials from multispectral remote sensing (RS) imagery with only several bands is an important issue that challenges comprehensive applications of the RS of surface characteristics. This study proposes a new method to identify spectrally similar materials from...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10849635/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859642651901952 |
|---|---|
| author | Maozhi Wang Shu-Hua Chen Jun Feng Wenxi Xu Daming Wang |
| author_facet | Maozhi Wang Shu-Hua Chen Jun Feng Wenxi Xu Daming Wang |
| author_sort | Maozhi Wang |
| collection | DOAJ |
| description | Identification of spectrally similar materials from multispectral remote sensing (RS) imagery with only several bands is an important issue that challenges comprehensive applications of the RS of surface characteristics. This study proposes a new method to identify spectrally similar materials from these types of imagery. The method is constructed based on the theory of condition number of matrix, and a theorem is proven as the foundation of the designed identification algorithm. Mathematically, the motivation behind designing this new algorithm is to decrease the condition number of the matrix for a linear system and, by doing so, to change an ill-conditioned system to a well-conditioned one. Technically, this new method achieves the purpose by adding supplementary features to all the original spectra including similar materials, which can be further used as indicative signatures to identify these materials. Thus, the proposed method is named a condition number-based method with supplementary features (SF-CNM). The threshold scheme and supplementary features are two main novelty techniques to ensure the uniqueness and accuracy of the proposed SF-CNM for specified samples. The results for a case study to identify water, ice, snow, shadow, and other materials from Landsat 8 OLI data indicate that SF-CNM can identify the materials specified by the given samples successfully and accurately and that SF-CNM significantly outperforms those of spectral angle mapper algorithm, Mahalanobis classifier, maximum likelihood, and artificial neural network, and produces the performance similar to, even slightly better than that of support vector machine. |
| format | Article |
| id | doaj-art-c9501f198bdd496a9fc570ff6297308d |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c9501f198bdd496a9fc570ff6297308d2025-02-11T00:00:33ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184751476610.1109/JSTARS.2025.353281610849635Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of MatrixMaozhi Wang0https://orcid.org/0000-0001-5233-4844Shu-Hua Chen1https://orcid.org/0000-0001-5929-1074Jun Feng2https://orcid.org/0000-0001-8066-5261Wenxi Xu3https://orcid.org/0000-0001-6298-7742Daming Wang4College of Mathematics Sciences, Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, ChinaDepartment of Land, Air, and Water Resources, University of California, Davis, CA, USACollege of Mathematics Sciences, Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, ChinaCollege of Mathematics Sciences, Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, ChinaTianjin Center of China Geological Survey, Tianjin, ChinaIdentification of spectrally similar materials from multispectral remote sensing (RS) imagery with only several bands is an important issue that challenges comprehensive applications of the RS of surface characteristics. This study proposes a new method to identify spectrally similar materials from these types of imagery. The method is constructed based on the theory of condition number of matrix, and a theorem is proven as the foundation of the designed identification algorithm. Mathematically, the motivation behind designing this new algorithm is to decrease the condition number of the matrix for a linear system and, by doing so, to change an ill-conditioned system to a well-conditioned one. Technically, this new method achieves the purpose by adding supplementary features to all the original spectra including similar materials, which can be further used as indicative signatures to identify these materials. Thus, the proposed method is named a condition number-based method with supplementary features (SF-CNM). The threshold scheme and supplementary features are two main novelty techniques to ensure the uniqueness and accuracy of the proposed SF-CNM for specified samples. The results for a case study to identify water, ice, snow, shadow, and other materials from Landsat 8 OLI data indicate that SF-CNM can identify the materials specified by the given samples successfully and accurately and that SF-CNM significantly outperforms those of spectral angle mapper algorithm, Mahalanobis classifier, maximum likelihood, and artificial neural network, and produces the performance similar to, even slightly better than that of support vector machine.https://ieeexplore.ieee.org/document/10849635/Condition numbermultispectral imageryspectrally similar materials identificationsupplementary features |
| spellingShingle | Maozhi Wang Shu-Hua Chen Jun Feng Wenxi Xu Daming Wang Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Condition number multispectral imagery spectrally similar materials identification supplementary features |
| title | Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix |
| title_full | Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix |
| title_fullStr | Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix |
| title_full_unstemmed | Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix |
| title_short | Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix |
| title_sort | identification of spectrally similar materials from multispectral imagery based on condition number of matrix |
| topic | Condition number multispectral imagery spectrally similar materials identification supplementary features |
| url | https://ieeexplore.ieee.org/document/10849635/ |
| work_keys_str_mv | AT maozhiwang identificationofspectrallysimilarmaterialsfrommultispectralimagerybasedonconditionnumberofmatrix AT shuhuachen identificationofspectrallysimilarmaterialsfrommultispectralimagerybasedonconditionnumberofmatrix AT junfeng identificationofspectrallysimilarmaterialsfrommultispectralimagerybasedonconditionnumberofmatrix AT wenxixu identificationofspectrallysimilarmaterialsfrommultispectralimagerybasedonconditionnumberofmatrix AT damingwang identificationofspectrallysimilarmaterialsfrommultispectralimagerybasedonconditionnumberofmatrix |