Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Abstract

Recently, deep learning methods have made significant progress in solving hyperspectral images (HSIs) classification problems of high-dimensional features, band redundancy, and spectral mixture. However, the deep neural network is too complex, with a long training time and high energy consumption, making it difficult to deploy on edge computing devices. In order to solve the above problems, this paper proposes a brain-inspired computing framework based on the spiking leaky integrate-andfire neuron model for HSIs classification. Then we design an approximate derivative algorithm to solve the non-differentiable spike activity of the spiking neuron. The framework uses direct coding to generate spatiotemporal spikes for input HSI and achieves efficient extraction of spatial-spectral features through spiking standard convolution and spiking depthwise separable convolution. Extensive experiments are performed on four benchmark hyperspectral data sets and two public unmanned aerial vehicle-borne hyperspectral data sets. Experiments show that the proposed model has the advantages of high classification accuracy and fewer spiking time steps. The proposed model can save about 10 times computational energy consumption compared with the CNN of the same architecture. This research has great significance for overcoming the technical bottleneck of HSI classification based on brain-inspired computing, solving the critical problems of mobile computing in unmanned autonomous systems, and realizing the engineering application of unmanned aerial vehicles and software-defined satellites. The source code will be made available at https://github.com/Katherine-Cao/HSI SNN.

Boundary-Aware Deformable Spiking Neural Network for Hyperspectral Image Classification

“…In this section, we choose four CNN-based methods (ResNet [38], DPyResNet [18], SSRN [19], and A2S2KResNet [20]) one deformable-CNN-based model (DHCNet [29]), and one SNN-based model (HSI-SNN [28]) for comparison. To fully prove the effectiveness of the proposed method, the experiment was performed on five benchmark data sets: Indian Pines(IP), Kennedy Space Center (KSC), Houston University (HU), Pavia University (PU), and Salinas (SV).…”

“…Datta et al [26] proposed a quantization-aware gradient descent method to train an SNN generated from iso-architecture CNNs for HSI classification. Liu et al [27,28] proposed two SNN classifiers based on channel shuffle attention mechanisms with two different derivative algorithms. These SNN models for HSI classification tend to fall into the trap of the Hughes phenomenon with fewer training samples.…”