Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching
Spectral unmixing in hyperspectral images involves determining endmembers and their associated abundance maps. The endmember estimate is extremely important in the processing of high resolution hyperspectral data. This study provides a unique automatic method for extracting endmembers by integrating...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10235321/ |
| _version_ | 1797688425970663424 |
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| author | M. R. Vimala Devi S. Kalaivani |
| author_facet | M. R. Vimala Devi S. Kalaivani |
| author_sort | M. R. Vimala Devi |
| collection | DOAJ |
| description | Spectral unmixing in hyperspectral images involves determining endmembers and their associated abundance maps. The endmember estimate is extremely important in the processing of high resolution hyperspectral data. This study provides a unique automatic method for extracting endmembers by integrating fuzzy clustering and a spectral-matching approach. The number of endmembers in an image is estimated in the first stage using the Hysime algorithm. Data are subsequently classified using a fuzzy c-means algorithm, which determines the grade of membership values for each data point. A threshold operation on membership values is used to select a collection of pixels as target pixels. Spectral matching aids in the selection of target pixels and searches for a specific endmember within a cluster. Endmember bundles are extracted from target pixels and compared with ground truth data using a spectral angle mapper. The performance of the proposed technique was evaluated in two ways: directly on full hyperspectral data and on dimension-reduced data by employing one simulated and two real datasets. The mean spectral angle and root mean square error were used as performance indicators. Furthermore, the accuracy of extracted endmembers was validated by creating abundance maps using a fully constrained least square technique, and the results were analyzed. |
| first_indexed | 2024-03-12T01:31:51Z |
| format | Article |
| id | doaj.art-0911621293514b98b714f9f555c11e4c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:31:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0911621293514b98b714f9f555c11e4c2023-09-11T23:01:55ZengIEEEIEEE Access2169-35362023-01-0111953159533310.1109/ACCESS.2023.331064910235321Endmember Estimation Using Fuzzy Grade of Membership and Spectral MatchingM. R. Vimala Devi0https://orcid.org/0000-0002-7515-8115S. Kalaivani1https://orcid.org/0000-0001-7177-4917School of Electronics Engineering, Vellore Institute of Technology (VIT University), Vellore, IndiaSchool of Electronics Engineering, Vellore Institute of Technology (VIT University), Vellore, IndiaSpectral unmixing in hyperspectral images involves determining endmembers and their associated abundance maps. The endmember estimate is extremely important in the processing of high resolution hyperspectral data. This study provides a unique automatic method for extracting endmembers by integrating fuzzy clustering and a spectral-matching approach. The number of endmembers in an image is estimated in the first stage using the Hysime algorithm. Data are subsequently classified using a fuzzy c-means algorithm, which determines the grade of membership values for each data point. A threshold operation on membership values is used to select a collection of pixels as target pixels. Spectral matching aids in the selection of target pixels and searches for a specific endmember within a cluster. Endmember bundles are extracted from target pixels and compared with ground truth data using a spectral angle mapper. The performance of the proposed technique was evaluated in two ways: directly on full hyperspectral data and on dimension-reduced data by employing one simulated and two real datasets. The mean spectral angle and root mean square error were used as performance indicators. Furthermore, the accuracy of extracted endmembers was validated by creating abundance maps using a fully constrained least square technique, and the results were analyzed.https://ieeexplore.ieee.org/document/10235321/Hyperspectral imagespectral unmixingendmember estimationfuzzy clusteringspectral matchingabundance maps |
| spellingShingle | M. R. Vimala Devi S. Kalaivani Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching IEEE Access Hyperspectral image spectral unmixing endmember estimation fuzzy clustering spectral matching abundance maps |
| title | Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching |
| title_full | Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching |
| title_fullStr | Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching |
| title_full_unstemmed | Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching |
| title_short | Endmember Estimation Using Fuzzy Grade of Membership and Spectral Matching |
| title_sort | endmember estimation using fuzzy grade of membership and spectral matching |
| topic | Hyperspectral image spectral unmixing endmember estimation fuzzy clustering spectral matching abundance maps |
| url | https://ieeexplore.ieee.org/document/10235321/ |
| work_keys_str_mv | AT mrvimaladevi endmemberestimationusingfuzzygradeofmembershipandspectralmatching AT skalaivani endmemberestimationusingfuzzygradeofmembershipandspectralmatching |