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Proceedings Paper

Remote sensing image segmentation using local sparse structure constrained latent low rank representation
Author(s): Shu Tian; Ye Zhang; Yimin Yan; Nan Su; Junping Zhang
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Paper Abstract

Latent low-rank representation (LatLRR) has been attached considerable attention in the field of remote sensing image segmentation, due to its effectiveness in exploring the multiple subspace structures of data. However, the increasingly heterogeneous texture information in the high spatial resolution remote sensing images, leads to more severe interference of pixels in local neighborhood, and the LatLRR fails to capture the local complex structure information. Therefore, we present a local sparse structure constrainted latent low-rank representation (LSSLatLRR) segmentation method, which explicitly imposes the local sparse structure constraint on LatLRR to capture the intrinsic local structure in manifold structure feature subspaces. The whole segmentation framework can be viewed as two stages in cascade. In the first stage, we use the local histogram transform to extract the texture local histogram features (LHOG) at each pixel, which can efficiently capture the complex and micro-texture pattern. In the second stage, a local sparse structure (LSS) formulation is established on LHOG, which aims to preserve the local intrinsic structure and enhance the relationship between pixels having similar local characteristics. Meanwhile, by integrating the LSS and the LatLRR, we can efficiently capture the local sparse and low-rank structure in the mixture of feature subspace, and we adopt the subspace segmentation method to improve the segmentation accuracy. Experimental results on the remote sensing images with different spatial resolution show that, compared with three state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

Paper Details

Date Published: 19 September 2016
PDF: 6 pages
Proc. SPIE 9976, Imaging Spectrometry XXI, 99760T (19 September 2016); doi: 10.1117/12.2237726
Show Author Affiliations
Shu Tian, Harbin Institute of Technology (China)
Ye Zhang, Harbin Institute of Technology (China)
Yimin Yan, Harbin Institute of Technology (China)
Nan Su, Harbin Institute of Technology (China)
Junping Zhang, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 9976:
Imaging Spectrometry XXI
John F. Silny; Emmett J. Ientilucci, Editor(s)

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