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Journal of Applied Remote Sensing

1/2-norm regularized nonnegative low-rank and sparse affinity graph for remote sensing image segmentation
Author(s): Shu Tian; Ye Zhang; Yiming Yan; Nan Su
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Paper Abstract

Segmentation of real-world remote sensing images is a challenge due to the complex texture information with high heterogeneity. Thus, graph-based image segmentation methods have been attracting great attention in the field of remote sensing. However, most of the traditional graph-based approaches fail to capture the intrinsic structure of the feature space and are sensitive to noises. A 1/2-norm regularization-based graph segmentation method is proposed to segment remote sensing images. First, we use the occlusion of the random texture model (ORTM) to extract the local histogram features. Then, a 1/2-norm regularized low-rank and sparse representation (L1/2NNLRS) is implemented to construct a 1/2-regularized nonnegative low-rank and sparse graph (L1/2NNLRS-graph), by the union of feature subspaces. Moreover, the L1/2NNLRS-graph has a high ability to discriminate the manifold intrinsic structure of highly homogeneous texture information. Meanwhile, the L1/2NNLRS representation takes advantage of the low-rank and sparse characteristics to remove the noises and corrupted data. Last, we introduce the L1/2NNLRS-graph into the graph regularization nonnegative matrix factorization to enhance the segmentation accuracy. The experimental results using remote sensing images show that when compared to five state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

Paper Details

Date Published: 1 August 2016
PDF: 21 pages
J. Appl. Rem. Sens. 10(4) 042008 doi: 10.1117/1.JRS.10.042008
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Shu Tian, Harbin Institute of Technology (China)
Ye Zhang, Harbin Institute of Technology (China)
Yiming Yan, Harbin Institute of Technology (China)
Nan Su, Harbin Institute of Technology (China)


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