Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image
Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral image (HSI). Band selection (BS), selecting o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5143 |
| _version_ | 1797684277402402816 |
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| author | Xuefeng Jiang Lin Zhang Junrui Liu Shuying Li |
| author_facet | Xuefeng Jiang Lin Zhang Junrui Liu Shuying Li |
| author_sort | Xuefeng Jiang |
| collection | DOAJ |
| description | Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral image (HSI). Band selection (BS), selecting only some representative bands to describe well the original image, is an appropriate approach to tackle this problem. In this study, the authors propose an efficient greedy‐based unsupervised BS method, namely the maximum simplex volume by orthogonal‐projection BS method. The main contributions are two‐fold: (i) an information‐lossless compressed descriptor in the Euclidean sense that can reduce the amount of redundant information in the band analysis and (ii) an orthogonal‐projection‐based algorithm to find the band points forming the simplex of maximum volume. The experimental results on four real HSIs demonstrate that the proposed method can achieve satisfying pixel classification performances and is computationally fast. |
| first_indexed | 2024-03-12T00:28:18Z |
| format | Article |
| id | doaj.art-8cd6054869774cc3b38a3ac83d43ac6a |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:28:18Z |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-8cd6054869774cc3b38a3ac83d43ac6a2023-09-15T10:31:50ZengWileyIET Computer Vision1751-96321751-96402019-03-0113223323910.1049/iet-cvi.2018.5143Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral imageXuefeng Jiang0Lin Zhang1Junrui Liu2Shuying Li3School of Computer Science, Northwestern Polytechnical UniversityXi'an710072ShaanxiPeople's Republic of ChinaSchool of Computer Science, Northwestern Polytechnical UniversityXi'an710072ShaanxiPeople's Republic of ChinaSchool of Computer Science, Northwestern Polytechnical UniversityXi'an710072ShaanxiPeople's Republic of ChinaSchool of AutomationXi'an University of Posts & TelecommunicationsXi'an710121ShaanxiPeople's Republic of ChinaHyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral image (HSI). Band selection (BS), selecting only some representative bands to describe well the original image, is an appropriate approach to tackle this problem. In this study, the authors propose an efficient greedy‐based unsupervised BS method, namely the maximum simplex volume by orthogonal‐projection BS method. The main contributions are two‐fold: (i) an information‐lossless compressed descriptor in the Euclidean sense that can reduce the amount of redundant information in the band analysis and (ii) an orthogonal‐projection‐based algorithm to find the band points forming the simplex of maximum volume. The experimental results on four real HSIs demonstrate that the proposed method can achieve satisfying pixel classification performances and is computationally fast.https://doi.org/10.1049/iet-cvi.2018.5143pixel classificationorthogonal-projection-based algorithmEuclidean senseinformation-lossless compressed descriptororthogonal-projection BS methodefficient greedy-based unsupervised BS method |
| spellingShingle | Xuefeng Jiang Lin Zhang Junrui Liu Shuying Li Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image IET Computer Vision pixel classification orthogonal-projection-based algorithm Euclidean sense information-lossless compressed descriptor orthogonal-projection BS method efficient greedy-based unsupervised BS method |
| title | Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image |
| title_full | Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image |
| title_fullStr | Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image |
| title_full_unstemmed | Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image |
| title_short | Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image |
| title_sort | maximum simplex volume an efficient unsupervised band selection method for hyperspectral image |
| topic | pixel classification orthogonal-projection-based algorithm Euclidean sense information-lossless compressed descriptor orthogonal-projection BS method efficient greedy-based unsupervised BS method |
| url | https://doi.org/10.1049/iet-cvi.2018.5143 |
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