Hyperspectral image segmentation using spectral-spatial constrained conditional random field

Airong Sun; Yihua Tan; Jinwen Tian

doi:10.1117/12.901789

8 December 2011 Hyperspectral image segmentation using spectral-spatial constrained conditional random field

Airong Sun, Yihua Tan, Jinwen Tian

Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 800213 (2011) https://doi.org/10.1117/12.901789
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Airong Sun, Yihua Tan, and Jinwen Tian "Hyperspectral image segmentation using spectral-spatial constrained conditional random field", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 800213 (8 December 2011); https://doi.org/10.1117/12.901789

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KEYWORDS

Image segmentation

Hyperspectral imaging

Image processing algorithms and systems

Data modeling

Image classification

Image processing

Process modeling

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