1 August 2016 1/2-norm regularized nonnegative low-rank and sparse affinity graph for remote sensing image segmentation
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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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shu Tian, Shu Tian, Ye Zhang, Ye Zhang, Yiming Yan, Yiming Yan, Nan Su, Nan Su, } "ℓ1/2-norm regularized nonnegative low-rank and sparse affinity graph for remote sensing image segmentation," Journal of Applied Remote Sensing 10(4), 042008 (1 August 2016). https://doi.org/10.1117/1.JRS.10.042008 . Submission:

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