Multiscanning Strategy-Based Recurrent Neural Network for Hyperspectral Image Classification

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

Research output: Contribution to journalArticlepeer-review

Abstract

Most methods based on convolutional neural network show satisfying performance for hyperspectral image (HSI) classification. However, the spatial dependency among different pixels is not well learned by CNNs. A recurrent neural network (RNN) can effectively establish the dependency of non-adjacent 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 dependency 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 dependency 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 dependency, 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.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
Publication statusAccepted/In press - 2021
Externally publishedYes

Keywords

  • Convolutional neural networks
  • deep learning
  • Deep learning
  • Feature extraction
  • Hyperspectral image classification
  • Hyperspectral imaging
  • multiscanning strategy
  • Principal component analysis
  • recurrent neural network
  • Recurrent neural networks
  • Task analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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