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Lightweight Hyperspectral Image Classification with Lifelong Learning
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  • Anran Yuan ,
  • Jing Bai ,
  • Zhu Xiao ,
  • Zhongrui Wang ,
  • Jianqing Li ,
  • Licheng Jiao
Anran Yuan
Macau University of Science and Technology

Corresponding Author:[email protected]

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Zhongrui Wang
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Jianqing Li
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Licheng Jiao
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With the increasing number of Hyperspectral Image (HSI) classification scenarios and frequent updates to classification categories, there is a growing demand for new models and updates to existing ones. In this paper, we develop a lightweight lifelong HSI classification model called Attention-based Reservoir Computing (ARC). This model eliminates the need for training and updating the feature extractor, largely reducing computational overhead and enabling fast adaptation to new classes. The proposed ARC’s superior performance, lightweight nature, and lifelong learning abilities promise to enhance the accuracy, efficiency, and adaptability of HSI classification, contributing to improved natural resource management, environmental monitoring, and disaster warning systems.