Endmember Estimation with Maximum Distance Analysis
Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/713 |
| _version_ | 1797396482231369728 |
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| author | Xuanwen Tao Mercedes E. Paoletti Juan M. Haut Peng Ren Javier Plaza Antonio Plaza |
| author_facet | Xuanwen Tao Mercedes E. Paoletti Juan M. Haut Peng Ren Javier Plaza Antonio Plaza |
| author_sort | Xuanwen Tao |
| collection | DOAJ |
| description | Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affine hull. The remaining endmembers are extracted by repetitively searching for the pixel points that satisfy the above three assumptions. In addition to behaving as an endmember estimation algorithm by itself, the MDA method can co-operate with existing endmember extraction techniques without the pure pixel assumption via generalizing them into more effective schemes. The conducted experiments validate the effectiveness and efficiency of our method on synthetic and real data. |
| first_indexed | 2024-03-09T00:50:57Z |
| format | Article |
| id | doaj.art-d8c36e0d74c049c28e2e921090462a38 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:50:57Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d8c36e0d74c049c28e2e921090462a382023-12-11T17:12:13ZengMDPI AGRemote Sensing2072-42922021-02-0113471310.3390/rs13040713Endmember Estimation with Maximum Distance AnalysisXuanwen Tao0Mercedes E. Paoletti1Juan M. Haut2Peng Ren3Javier Plaza4Antonio Plaza5Hyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10002 Caceres, SpainHyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10002 Caceres, SpainDepartment of Communication and Control Systems, Higher Technical School of Computer Engineering, National Distance Education University (UNED), E-28040 Madrid, SpainCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaHyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10002 Caceres, SpainHyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10002 Caceres, SpainEndmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affine hull. The remaining endmembers are extracted by repetitively searching for the pixel points that satisfy the above three assumptions. In addition to behaving as an endmember estimation algorithm by itself, the MDA method can co-operate with existing endmember extraction techniques without the pure pixel assumption via generalizing them into more effective schemes. The conducted experiments validate the effectiveness and efficiency of our method on synthetic and real data.https://www.mdpi.com/2072-4292/13/4/713hyperspectral imagespectral unmixingmaximum distance analysisendmember extractionendmember estimation |
| spellingShingle | Xuanwen Tao Mercedes E. Paoletti Juan M. Haut Peng Ren Javier Plaza Antonio Plaza Endmember Estimation with Maximum Distance Analysis Remote Sensing hyperspectral image spectral unmixing maximum distance analysis endmember extraction endmember estimation |
| title | Endmember Estimation with Maximum Distance Analysis |
| title_full | Endmember Estimation with Maximum Distance Analysis |
| title_fullStr | Endmember Estimation with Maximum Distance Analysis |
| title_full_unstemmed | Endmember Estimation with Maximum Distance Analysis |
| title_short | Endmember Estimation with Maximum Distance Analysis |
| title_sort | endmember estimation with maximum distance analysis |
| topic | hyperspectral image spectral unmixing maximum distance analysis endmember extraction endmember estimation |
| url | https://www.mdpi.com/2072-4292/13/4/713 |
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