Sub-Band Grouping Spectral Feature-Attention Block for Hyperspectral Image Classification

Weilian Zhou, Sei Ichiro Kamata, Zhengbo Luo

Research output: Contribution to journalConference articlepeer-review

Abstract

Hyperspectral images (HSIs) consists of 2D spatial information and 1D spectral signature due to its specialty. Most models take the raw spectral signature as the input directly by regarding the spectral data as a sequence, which cannot fully explore the redundant and complementary information inside the spectral bands. In this paper, we proposed a novel sub-band grouping recurrent neural network (RNN) model with gated recurrent units (GRUs) to find the intrinsic feature in spectral information. We introduced the inter-band spectral cross-correlation measurement to see the high correlated groups of adjacent bands firstly. And then we concatenated the representative features from all groups for complementarity. The novel spectral feature-attention block was proposed to compound the mentioned steps and generated a much sparser feature representation for subsequent analysis. The experiment results illustrated the outstanding performances and got almost 1% and 5% improvement compared with the latest methods on two famous datasets.

Original languageEnglish
Pages (from-to)1820-1824
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 2021 Jun 62021 Jun 11

Keywords

  • Group of bands
  • Hyperspectral image classification
  • Inter-band spectral cross-correlation
  • Recurrent neural network

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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