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- Cao, Xianghai; Li, Renjie; Ge, Yiming; Wu, Bin; Jiao, Licheng
- International journal of remote sensing 2019 v.40 no.9 pp. 3606-3622
- algorithms; hyperspectral imagery; models; remote sensing
- ... In very recent years, deep learning based methods have been widely introduced for the classification of hyperspectral images (HSI). However, these deep models need lots of training samples to tune abundant parameters which induce a heavy computation burden. Therefore, most of these algorithms need to be accelerated with high-performance graphics processing units (GPU). In this paper, a new deep mo ...
- Xie, Fuding; Lei, Cunkuan; Li, Fangfei; Huang, Dan; Yang, Jun
- International journal of remote sensing 2019 v.40 no.9 pp. 3344-3367
- Aesculus; algorithms; data collection; hyperspectral imagery; remote sensing; selection methods; wolves
- ... Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective fe ...