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Proceedings Paper

Hyperspectral image classification by collaboration of spatial and spectral information
Author(s): Yu-zhou Yan; Yongqiang Zhao; Hui-feng Xue; Xiao-dong Kou; Yuanzheng Liu
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Paper Abstract

The classification of hyperspectral image data has drawn much attention in recent years. Consequently, it contains not only spectral information of objects, but also spatial arrangement of objects. The most established Hyperspectral classifiers are based on the observed spectral signal, and ignore the spatial relations among observations. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. To combine the spectral and spatial information in the classification process, in this paper, a Multidimensional Local Spatial Autocorrelation (MLSA) is proposed for hyperspectral image data. Based on this measure, a collaborative classification method is proposed, which integrates the spectral and spatial autocorrelation during the decision-making process. The trials of our experiment are conducted on two scenes, one from HYDICE 210-band imagery collected over an area that contains a diverse range of terrain features and the other is toy car hyperspectral image captured at Instrumentation and Sensing Laboratory (ISL) at Beltsville Agricultural Research Center. Quantitative measures of local consistency (smoothness) and global labeling, along with class maps, demonstrate the benefits of applying this method for unsupervised and supervised classification.

Paper Details

Date Published: 5 August 2009
PDF: 5 pages
Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73834B (5 August 2009); doi: 10.1117/12.839633
Show Author Affiliations
Yu-zhou Yan, Northwestern Polytechnical Univ. (China)
Yongqiang Zhao, Northwestern Polytechnical Univ. (China)
Hui-feng Xue, Northwestern Polytechnical Univ. (China)
Xiao-dong Kou, Northwestern Polytechnical Univ. (China)
Yuanzheng Liu, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 7383:
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications
Jeffery Puschell; Hai-mei Gong; Yi Cai; Jin Lu; Jin-dong Fei, Editor(s)

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