23 April 2012 Local combination histogram based segmentation of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus images using clustering aggregation
Wei Yang, Kun Hou, Fanhua Yu, Tieli Sun
Author Affiliations +
Abstract
We present a new segmentation method for multispectral remote sensing imagery using the K-means clustering aggregation coupled with local combination histogram (LCH). First of all, the bands are partitioned into several nearly uncorrelated subsets. Then, some band combinations of the multispectral images are generated from the subsets. After that, the LCHs are computed from quantized images in each band combination. The LCH represents a pixel in the sense of both spectral information and neighborhood spatial information implicitly and serves as the input feature. Identical K-means procedures are employed to get several relatively coarse segmentation maps. The final K-means procedure refines these intermediate segmentation maps to achieve the final results. The segmentation results are evaluated and compared with other multispectral image segmentation methods by visual inspection and object-based image classification. Experimental results show that the proposed method can achieve more accurate segmentation maps and higher classification accuracy.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2012/$25.00 © 2012 SPIE
Wei Yang, Kun Hou, Fanhua Yu, and Tieli Sun "Local combination histogram based segmentation of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus images using clustering aggregation," Optical Engineering 51(4), 046201 (23 April 2012). https://doi.org/10.1117/1.OE.51.4.046201
Published: 23 April 2012
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Image segmentation

Earth observing sensors

Landsat

Multispectral imaging

Image classification

Remote sensing

Optical engineering

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