Toward Effective Hyperspectral Image Classification Using Dual-Level Deep Spatial Manifold Representation

Hyperspectral image (HSI) contains an abundant spatial structure that can be embedded into feature extraction (FE) or classifier (CL) components for pixelwise classification enhancement. Although some existing works have exploited some simple spatial structures (e.g., local similarity) to enhance either the FE or CL component, few of them consider the latent manifold structure and how to simultaneously embed the manifold structure into both components seamlessly. Thus, their performance is still limited, especially in cases with limited or noisy training samples. To solve both problems with one stone, we present a novel dual-level deep spatial manifold representation (SMR) network for HSI classification, which consists of two kinds of blocks: an SMR-based FE block and an SMR-based CL block. In both blocks, graph convolution is utilized to adaptively model the latent manifold structure lying in each local spatial area. The difference is that the former block condenses the SMR in deep feature space to form the representation for each center pixel, while the later block leverages the SMR to propagate the label information of other pixels within the local area to the center one. To train the network well, we impose an unsupervised information loss on unlabeled samples and a supervised cross-entropy loss on the labeled samples for joint learning, which empowers the network to utilize sufficient samples for SMR learning. Extensive experiments on two benchmark HSI data set demonstrate the efficacy of the proposed method in terms of pixelwise classification, especially in the cases with limited or noisy training samples.