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Related Experiment Video

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An Effective Hyperspectral Image Classification Network Based on Multi-Head Self-Attention and Spectral-Coordinate

Minghua Zhang1, Yuxia Duan1, Wei Song1

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.

Journal of Imaging
|July 28, 2023
PubMed
Summary

This study introduces a new hyperspectral image (HSI) classification network using multi-head self-attention and spectral-coordinate attention. The method enhances accuracy and efficiency without increasing computational cost for HSI classification.

Keywords:
deep learninghyperspectral imageimage classificationlong-range dependencyspectral-coordinate attention

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Hyperspectral image (HSI) classification is crucial for analyzing spectral data.
  • Convolutional Neural Networks (CNNs) show promise but struggle with accuracy and efficiency due to limited receptive fields and deep architectures.
  • Existing methods often face challenges in balancing performance and computational load for HSI classification.

Purpose of the Study:

  • To propose an effective hyperspectral image classification network that overcomes the limitations of CNN-based methods.
  • To enhance both the accuracy and efficiency of HSI classification.
  • To introduce a novel network architecture that integrates multi-head self-attention and spectral-coordinate attention.

Main Methods:

  • A point-wise convolution network (PCN) is utilized to reduce spectral redundancy and improve discriminability.
  • A modified multi-head self-attention (M-MHSA) model with down-sampling is employed to capture long-range dependencies efficiently.
  • A lightweight spectral-coordinate attention fusion module combining spectral attention (SA) and coordinate attention (CA) is introduced to enhance feature weighting and object localization.

Main Results:

  • The proposed MSSCA network demonstrates competitive performance on Indian Pines (IP), Pavia University (PU), and Salinas HSI datasets.
  • Experimental results indicate significant improvements in classification accuracy compared to existing methods.
  • The method achieves these accuracy gains without an increase in network complexity or computational cost.

Conclusions:

  • The proposed multi-head self-attention and spectral-coordinate attention network (MSSCA) offers an effective solution for accurate and efficient HSI classification.
  • The integration of PCN, M-MHSA, and spectral-coordinate attention fusion module successfully addresses the limitations of traditional CNNs.
  • The method presents a highly competitive approach for HSI classification tasks, balancing performance and computational efficiency.