Multi-scanning based recurrent neural network for hyperspectral image classification

Weilian Zhou, Seiichiro-Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As the specialty of hyperspectral image (HSI), it consists of 2D spatial and 1D spectral information. In the field of deep learning, HSI classification is an appealing research topic. Many existing methods process the HSI in spatial or spectral domain separately, which cannot fully extract the representative features, and the most used 3D convolutional neural network (3D-CNN) will suffer from mixing up complex spectral information. In this paper, we propose a spatial-spectral unified method by using recurrent neural networks (RNN) and multi-scanning direction strategy to construct spatial-spectral information sequences for learning the spatial dependencies among the central pixel and neighboring pixels. Meanwhile, residual connections and dense connections are introduced into multi-scanning direction sequences to overcome the memory problem in the RNN. The proposed method got 99.58% and 99.81% accuracy respectively on two benchmark datasets: the Pavia University dataset and the Pavia Center dataset. It demonstrates the proposed method can achieve state-of-the-art results.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728188089
Publication statusPublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: 2021 Jan 102021 Jan 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference25th International Conference on Pattern Recognition, ICPR 2020
CityVirtual, Milan


  • Hyperspectral image classification
  • Multi-scanning
  • Recurrent neural network
  • Spatial-spectral sequences

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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