Details

Autor(en) / Beteiligte

• Zhu, Guokang
• Huang, Yuancheng
• Li, Shuying
• Tang, Jun
• Liang, Dong

Titel

Hyperspectral Band Selection via Rank Minimization

Ist Teil von

• IEEE geoscience and remote sensing letters, 2017-12, Vol.14 (12), p.2320-2324

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2017

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• Band selection is an important preprocessing technique for hyperspectral imagery, through which a subset of critical and representative spectral bands can be selected from a raw image cube for compact yet effect representation. Among the valid selection strategies, performing band selection in an unsupervised manner is usually considered more general due to its application-independent characteristic. This letter proposed a novel unsupervised hyperspectral band selector that can capture the interband redundancy nature of hyperspectral images through low-rank modeling. Experiments on three real-world hyperspectral data sets demonstrated that the proposed band selector can generate band subsets suitable in the context of hyperspectral pixel classification.