CBANet: An End-to-end Cross Band 2-D Attention Network for Hyperspectral Change Detection in Remote Sensing

Yinhe Li, Jinchang Ren, Yijun Yan, Qiaoyuan Liu, Ping Ma, Andrei Petrovski, Haijiang Sun

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
13 Downloads (Pure)

Abstract

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology.

Original languageEnglish
Article number5513011
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume61
DOIs
Publication statusPublished - 16 May 2023

Keywords

  • change detection
  • Convolutional neural networks
  • cross-band self-attention network (CBANet)
  • Data mining
  • Feature extraction
  • hyperspectral images (HSI)
  • Hyperspectral imaging
  • Kernel
  • spatial-spectral feature extraction
  • Spectral analysis
  • Task analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

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