High-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement

Limited by the existing imagery hardware and contaminated with noise or shading, spatial deterioration and spectral distortion exist in hyperspectral images (HSIs). Spectral-spatial quality enhancement was seldom addressed in a clear way albeit of first importance in HSI interpretation. In this paper, we present a promising quality enhancement method in a spectral-spatial combination framework for removing unwanted components and enhancing useful features. Our approach, called saliency detection and feature enhancement (SDFE), combines the theory of structure tensor with a deep convolutional neural network (CNN) to solve an HSI quality enhancement problem that has rarely been identified. Considering the different contribution rates of each band, an adaptive weighting method based on the eigenvalues of structure tensor is proposed to fuse the selected key band group. Then, a saliency detection method is presented to extract edge areas and corners. Owning to the success of CNN in visual-based issues, we utilize it to further enhance the saliency and obtain high-quality spatial information. To extract high-quality spectral features, the nonnegative matrix factorization (NMF) algorithm is used to extract spectral information from the original HSI. The experimental result enjoys a fact of identical materials with the similar signatures, which is useful for the subsequent application. Furthermore, our approach has a powerful influence on target detection. (C) 2018 Elsevier Ltd. All rights reserved.