Abstract

Hyperspectral image (HSI) classification has been a hot topic in the remote sensing community. A large number of methods have been proposed for HSI classification. However, most of them are based on the extraction of spectral feature, which leads to information loss. Moreover, they rarely consider the correlation among the spectrums. In this paper, we see spectral information as a sequential data which should be relevant to each other. We introduce long short-term memory (LSTM) model, which is a typical recurrent neural network (RNN), to deal with HSI classification. To tackle the problem of overfitting caused by limited labeled samples, regularization strategy is introduced. For unbalance in different classes, we improve LSTM by weighted cost function. Also, we employ guided filter to smooth the HSI that can greatly improve the classification accuracy. And we proposed a method for modeling hyperspectral sequential data, which is very useful for future research work. Finally, the experimental results show that our proposed method can improve the classification performance as compared to other methods in three popular hyperspectral datasets.

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Cite this article

@article{cai-5019,
  author  = {Yanhui Guo and Fuli Qu and Zhenmei Yu and Qian Yu},
  title   = {Deep LSTM with Guided Filter for Hyperspectral Image Classification},
  journal = {Computing and Informatics},
  volume  = {39},
  number  = {5},
  pages   = {973--993},
  year    = {2020},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2020_5_973}
}
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