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Proceedings Paper

Research on segmentation method of hyperspectral remote sensing images based on probabilistic neural networks
Author(s): Gang Liu; Xingjian Liu
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Paper Abstract

Image segmentation is essential for information extraction from remote sensing image, but it remains the lacks of a general mathematical theory, object merging for poor object boundary localization, dealing with object fragmentation and sensitivity of current procedures to noise. This paper focuses on hyper-spectral image segmentation using probabilistic neural networks (PNN). The methodology, implementation and optimization of a PNN are studied, and a constructed PNN is applied to segment hyper-spectral image. The experience demonstrates main advantage of a PNN that it has quick training and learning, gives a measurement of confidence associated with an output, and has the ability to process large data set. It is concluded that the PNN is superior in image segmentation and the obtained results are satisfied.

Paper Details

Date Published: 29 December 2008
PDF: 8 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72851J (29 December 2008); doi: 10.1117/12.815625
Show Author Affiliations
Gang Liu, Wuhan Univ. (China)
Xingjian Liu, Texas State Univ. (United States)


Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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