An Efficient Spectral Feature Extraction Framework for Hyperspectral Images

Abstract

Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accuracy. In this paper, we design an effective feature extracting framework for the spectra of hyperspectral data. We construct a structured dictionary to encode spectral information and apply learning machine to map coding coefficients. To reduce training and testing time, the sparsity constraint is replaced by a block-diagonal constraint to accelerate the iteration, and an efficient extreme learning machine is employed to fit the spectral characteristics. To optimize the discriminative ability of our model, we first add spectral convolution to extract abundant spectral information. Then, we design shared constraints for subdictionaries so that the common features of subdictionaries can be expressed more effectively, and the discriminative and reconstructive ability of dictionary will be improved. The experimental results on diverse databases show that the proposed feature extraction framework can not only greatly reduce the training and testing time, but also lead to very competitive accuracy performance compared with deep learning models.