Boundary-Aware Deformable Spiking Neural Network for Hyperspectral Image Classification

Abstract

A few spiking neural network (SNN)-based classifiers have been proposed for hyperspectral images (HSI) classification to alleviate the higher computational energy cost problem. Nevertheless, due to the lack of ability to distinguish boundaries, the existing SNN-based HSI classification methods are very prone to falling into the Hughes phenomenon. The confusion of the classifier at the class boundary is particularly obvious. To remedy these issues, we propose a boundary-aware deformable spiking residual neural network (BDSNN) for HSI classification. A deformable convolution neural network plays the most important role in realizing the boundary-awareness of the proposed model. To the best of our knowledge, this is the first attempt to combine the deformable convolutional mechanism and the SNN-based model. Additionally, spike-element-wise ResNet is used as a fundamental framework for going deeper. A temporal channel joint attention mechanism is introduced to filter out which channels and times are critical. We evaluate the proposed model on four benchmark hyperspectral data sets—the IP, PU, SV, and HU data sets. The experimental results demonstrate that the proposed model can obtain a comparable classification accuracy with state-of-the-art methods in terms of overall accuracy (OA), average accuracy (AA), and statistical kappa (κ) coefficient. The ablation study results prove the effectiveness of the introduction of the deformable convolutional mechanism for BDSNN’s boundary-aware characteristic.