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Proceedings Paper

Unsupervised segmentation of high-resolution satellite imagery using local spectral and texture features
Author(s): QiuXiao Chen; JianCheng Luo; ChengHu Zhou
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Paper Abstract

In many cases, segmentation approaches for remotely sensed imagery only deal with grey values, which makes them incompetent for segmenting high resolution imagery because texture features are more clearly displayed on them. On the other hand, texture segmentation approaches utilizing both spectral and texture features are, however, very complicated and time consuming which prevents their application. Therefore, to develop simple and effective segmentation approaches for high resolution satellite imagery is very important. In this article, a simple unsupervised segmentation approach for high resolution satellite imagery is proposed. First, wavelet decomposition is utilized to downsample each band of a multiband image. Then a gradient criterion to incorporate local spectral and texture features is utilized to produce a gradient feature image in which pixels with the high and low values correspond to region boundaries and region interiors respectively. Subsequently, a watershed segmentation approach is implemented based on the gradient feature image. Finally, by taking a strategy to minimize the overall heterogeneity increased within segments at each merging step, an improved merging process is performed. Experiments on Quickbird images show that the proposed method provides good segmentation results on high resolution satellite imagery.

Paper Details

Date Published: 2 November 2004
PDF: 11 pages
Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); doi: 10.1117/12.561328
Show Author Affiliations
QiuXiao Chen, Zhejiang Univ. (China)
State Key Lab. of Resources and Environmental Information Systems, CAS (China)
JianCheng Luo, State Key Lab. of Resources and Environmental Information Systems, CAS (China)
ChengHu Zhou, State Key Lab. of Resources and Environmental Information Systems, CAS (China)


Published in SPIE Proceedings Vol. 5558:
Applications of Digital Image Processing XXVII
Andrew G. Tescher, Editor(s)

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