Adaptive Feature Self-Attention in Spiking Neural Networks for Hyperspectral Classification

Hyperspectral image (HSI) classification is crucial for remote sensing research, while its high-dimensional features make traditional algorithms difficult to cope with. Despite the breakthroughs in deep learning, the high computational complexity and energy consumption limit its application in resource-limited environments. Spiking neural networks (SNNs), mimicking the brain's information processing with low power consumption, have emerged as a promising alternative for edge computing. However, SNNs struggle with complex tasks due to the nondifferentiability of spike signals, which complicates training and exhibits limitations in extracting deep features and modeling long-range dependencies. In this article, we propose a novel SNN framework that addresses these challenges by enhancing feature extraction and efficiently capturing dependencies in hyperspectral data. Our framework integrates an adaptive refocusing convolutional layer with a spike self-attention (SSA) mechanism. The adaptive refocusing convolutional layer employs learnable parameters to dynamically adjust the convolutional kernel's response to input spike data, improving feature representation. The adaptive refocusing convolutional layer uses learnable parameters to dynamically adjust kernel responses to input spike data, enhancing feature representation. Experimental results show that this model achieves over 96% classification accuracy in a single time step, significantly surpassing current methods and effectively solving the problem of low accuracy at short time steps in SNNs. Additionally, this framework reduces computational energy consumption by approximately 12.5x compared to similar, offering new potential for edge intelligence applications.