Compressive Sensing-Inspired Dual-Sparse SLFNN for Hyperspectral Imagery Classification

Abstract

In this letter we explore the sparse sensing and learning mechanism of the human visual system, to propose a dual-sparse single-hidden-layer feedforward neural network (SLFNN) for the hyperspectral imagery classification. Firstly a large SLFNN is randomly initialized and trained by an extreme learning algorithm, and then the input and hidden neurons are simultaneously reduced by imposing a sparse constraint on the weights of the network. Then a saliency map is derived via the recent developed compressive sensing theory, and a joint sparse optimization approach is proposed to realize a one-step rapid selection of significant neurons. The reduction of input neurons can realize an automatic band-subset-selection of hyperspectral bands to remove the redundancy of hyperspectral vectors, and the reduction of hidden neurons can avoid the high computational cost at runtime and potential overfitting. Some experiments are taken on AVIRIS imagery data to investigate the performance of the proposed method, and the results show that it can achieve accurate and rapid classification.