  Hyper spectral images (HSI) provide rich spectral and spatial information
across a series of contiguous spectral bands. However, the accurate processing
of the spectral and spatial correlation between the bands requires the use of
energy-expensive 3-D Convolutional Neural Networks (CNNs). To address this
challenge, we propose the use of Spiking Neural Networks (SNNs) that are
generated from iso-architecture CNNs and trained with quantization-aware
gradient descent to optimize their weights, membrane leak, and firing
thresholds. During both training and inference, the analog pixel values of a
HSI are directly applied to the input layer of the SNN without the need to
convert to a spike-train. The reduced latency of our training technique
combined with high activation sparsity yields significant improvements in
computational efficiency. We evaluate our proposal using three HSI datasets on
a 3-D and a 3-D/2-D hybrid convolutional architecture. We achieve overall
accuracy, average accuracy, and kappa coefficient of 98.68%, 98.34%, and 98.20%
respectively with 5 time steps (inference latency) and 6-bit weight
quantization on the Indian Pines dataset. In particular, our models achieved
accuracies similar to state-of-the-art (SOTA) with 560.6 and 44.8 times less
compute energy on average over three HSI datasets than an iso-architecture
full-precision and 6-bit quantized CNN, respectively.
