Deep Manifold Structure-Preserving Spectral–Spatial Feature Extraction of Hyperspectral Image

Abstract

The deep network has shown its superiority to extract discriminative features for hyperspectral image (HSI) classification. However, most existing methods only exploit label information of land classes to supervise the learning of deep features, and the learning process is totally automatic. Considering the complex spectral-spatial characteristic in real HSI, it is reasonable to utilize the manifold structure of input data to provide complementary information for precise classification. This article proposes a structure-preserving spectral-spatial network (SPSSN) to exploit the manifold structure information during the feature learning process, which can extract discriminative deep structure-preserving spectral-spatial features. Specifically, a basic spectral-spatial network (SSN), comprising a spectral module and a spatial module connected in series, is first built to learn spectral-spatial features from HSI cubes. Second, a novel structure-preserving constraint is introduced to transfer the intrinsic manifold structure of the input data into the learned deep spectral-spatial features, so as to enhance the discrimination. Furthermore, a feasible algorithm of imposing the constraint is designed to make it compatible with the deep learning framework, which results in a structure-preserving loss. Finally, the SSN is combined with the structure-preserving loss to obtain the SPSSN. Experiments on real HSI datasets verify the effectiveness and superiority of the SPSSN compared with several state-of-the-art methods in HSI classification.