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Proceedings Paper

Hyperspectral image segmentation using spectral-spatial constrained conditional random field
Author(s): Airong Sun; Yihua Tan; Jinwen Tian
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Paper Abstract

In this paper, we propose a hyperspectral image segmentation algorithm which combines classification and segmentation into Conditional Random Field(CRF) framework. The classification step is implemented using Gaussian process which gives the exact class probabilities of a pixel. The classification result is treated as the single-pixel model in CRF framework, by which classification and segmentation are combined together. Through the CRF, the spatial and spectral constraints on pixel classification are exploited. As a result, experimental results on real hyperspectral image show that the segmentation precision has been much improved.

Paper Details

Date Published: 8 December 2011
PDF: 8 pages
Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 800213 (8 December 2011); doi: 10.1117/12.901789
Show Author Affiliations
Airong Sun, Huazhong Univ. of Science and Technology (China)
Wuhan Institute of Technology (China)
Yihua Tan, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8002:
MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis
Faxiong Zhang; Faxiong Zhang, Editor(s)

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