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Hyperspectral Imagery Clustering With Neighborhood Constraints

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6 Author(s)
Shanshan Li ; Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China ; Bing Zhang ; An Li ; Xiuping Jia
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This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area. A new similarity measurement integrates NHI and spectral information using an adaptive distance norm for clustering. The performance of the proposed neighborhood-constrained-clustering algorithm was assessed through a synthetic image and a real hyperspectral image and compared with those obtained by advanced spectral-spatial clustering algorithms. Experimental results show that the proposed scheme gives better performances.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:10 ,  Issue: 3 )