Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification. (September 2018)

• Record Type: Journal Article
• Authors: Shao, Yuanjie; Sang, Nong; Gao, Changxin; Ma, Li
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 81
• Page End: 94
• Publication Date: 2018-09
• Subjects: Spatial regularization -- Probabilistic class structure -- Sparse representation (SR) -- Semi-supervised learning (SSL) -- Hyperspectral image (HSI) classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.03.027
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Sparse representation based edge weighting method is employed in the graph based SSL. Spatial neighborhood information and probabilistic class structure are both incorporated into the sparse representation model. The proposed graph construction method is superior to state of the arts methods. Abstract: Constructing a good graph that can capture intrinsic data structures is critical for graph-based semi-supervised learning methods, which are widely applied for hyperspectral image (HSI) classification with small amount of labeled samples. Among the existing graph construction methods, sparse representation (SR)-based methods have shown impressive performance on semi-supervised HSI classification tasks. However, most SR-based algorithms fail to consider the rich spatial information of HSI, which has been shown beneficial for classification tasks. In this paper, we propose a spatial and class structure regularized sparse representation (SCSSR) graph for semi-supervised HSI classification. Specifically, spatial information has been incorporated into SR model via the graph Laplacian regularization, it assumes that the spatial neighbors should have similar representation coefficients, the obtained coefficient matrix thus can reflect the similarity between samples more accurately. Besides, we also incorporate probabilistic class structure, which implies the probabilistic relationship between each sample and each class, into SR model to further improve discriminability of graph. The experimental results on Hyperion and AVIRIS hyperspectral data show that our method outperforms state of the art methods.