Multiscanning Strategy-Based Recurrent Neural Network for Hyperspectral Image Classification

Weilian Zhou, Sei Ichiro Kamata, Zhengbo Luo, Haipeng Wang

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Most methods based on the convolutional neural network show satisfying performance for hyperspectral image (HSI) classification. However, the spatial dependence among different pixels is not well learned by CNNs. A recurrent neural network (RNN) can effectively establish the dependence of nonadjacent pixels and ensure that each feature activation in its output is an activation at the specific location concerning the whole image, in contrast to the usual local context window in the CNNs. However, recent limited conversion schemes in RNN-based methods for HSI classification cannot fully capture the complete spatial dependence of an HSI patch. In this study, a novel multiscanning strategy with RNN is proposed to feature the sequential character of the HSI pixel and fully consider the spatial dependence in the HSI patch. By investigating different scanning forms, eight scanning orders are considered spatially, which flattens one local HSI patch into eight neighboring continuous pixel sequences. Moreover, considering that eight scanning orders complement one local patch with correlative dependence, the concatenated features from all scanning orders are fed into the RNN again for complementarity. As a result, the network can achieve competitive classification performance on three publicly accessible datasets using fewer parameters than other state-of-the-art methods.

本文言語English
ジャーナルIEEE Transactions on Geoscience and Remote Sensing
60
DOI
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 地球惑星科学(全般)

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