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Convolutional Neural Network- (CNN-) based land cover classification algorithms have recently been applied in hyperspectral images (HSI) field. However, the large-scale training parameters bring huge computation burden to CNN and the spatial variability of spectral signatures leads to relative low classification accuracy. In this paper, we propose a CNN-based classification framework that extracts square matrix representation-based spectral-spatial features and performs land cover classification. Numerical results on popular datasets show that our framework outperforms sparsity-based approaches like basic thresholding classifier-weighted least squares (BTC-WLS) and other deep learning-based methods in terms of both classification accuracy and computational cost.

FMRSS Net: Fast Matrix Representation-Based Spectral-Spatial Feature Learning Convolutional Neural Network for Hyperspectral Image Classification