Multi-Scale Fusion Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace clustering is combined as a self-expression layer with stacked convolutional autoencoder, so that subspace clustering working in linear subspaces c...

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Vydané v:	IEEE International Geoscience and Remote Sensing Symposium proceedings s. 779 - 782
Hlavní autori:	Ma, Haipeng, Wang, Yulei, Jiang, Liru, Song, Meiping, Yu, Chunyan, Zhao, Enyu
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 17.07.2022
Predmet:	Benchmark testing Data mining Decoding Entropy Geoscience and remote sensing hyperspectral band selection Hyperspectral imaging maximum entropy regularization multi-scale fusion stacked convolutional autoencoder subspace clustering
ISSN:	2153-7003
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