Hyperspectral Band Selection: A Review
A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational...
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| Vydané v: | IEEE geoscience and remote sensing magazine Ročník 7; číslo 2; s. 118 - 139 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.06.2019
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| Predmet: | |
| ISSN: | 2473-2397, 2168-6831 |
| On-line prístup: | Získať plný text |
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| Abstract | A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral information of ground objects. In this article, we review current hyperspectral band selection methods, which can be classified into six main categories: ranking based, searching based, clustering based, sparsity based, embedding-learning based, and hybrid-scheme based. With two widely used hyperspectral data sets, we illustrate the classification performances of several popular band selection methods. The challenges and research directions of hyperspectral band selection are also discussed. |
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| AbstractList | A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral information of ground objects. In this article, we review current hyperspectral band selection methods, which can be classified into six main categories: ranking based, searching based, clustering based, sparsity based, embedding-learning based, and hybrid-scheme based. With two widely used hyperspectral data sets, we illustrate the classification performances of several popular band selection methods. The challenges and research directions of hyperspectral band selection are also discussed. |
| Author | Du, Qian Sun, Weiwei |
| Author_xml | – sequence: 1 givenname: Weiwei orcidid: 0000-0003-3399-7858 surname: Sun fullname: Sun, Weiwei email: nbsww@outlook.com organization: Ningbo University, Zhejiang, China – sequence: 2 givenname: Qian orcidid: 0000-0001-8354-7500 surname: Du fullname: Du, Qian email: du@ece.msstate.edu organization: Electrical and Computer Engineering, Mississippi State University, Starkville, Mississippi United States |
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| CODEN | IGRSCZ |
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| Title | Hyperspectral Band Selection: A Review |
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