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Journal of Applied Remote Sensing

Adaptive regional feature extraction for very high spatial resolution image classification
Author(s): Leiguang Wang; Qinling Dai; Liang Hong; Guoying Liu
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Paper Abstract

An object-oriented, multiscale feature extraction approach is proposed for the land-cover classification of high spatial resolution images. The approach provides more discriminative features by considering the spatial context information from different segmentation levels. It consists of three successive substeps: segmentation by mean-shift algorithm, an iteratively merging process controlled by merging cost function and range-of-scale parameter, and feature extraction from linked multilevel image partitions. The mean-shift method is to get boundary-preserved and spectrally homogeneous over-segmentation regions. Then, a family of nested image partitions is constructed by a merging procedure. Meanwhile, every region of the finest scale is linked to image objects of its superlevels. Finally, every region in the finest scale is treated as a basic analysis unit, and the feature vectors are created by stacking statistics from the region and their superlevels. A support vector machine is used as a classifier and the method on two widely used high spatial resolution data sets over Pavia City, Italy, are evaluated. Compared with results reported in many papers, the result indicates superior accuracy.

Paper Details

Date Published: 7 March 2012
PDF: 17 pages
J. Appl. Rem. Sens. 6(1) 063506 doi: 10.1117/1.JRS.6.063506
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Leiguang Wang, Southwest Forestry Univ. (China)
Qinling Dai, Southwest Forestry Univ. (China)
Liang Hong, Yunnan Normal Univ. (China)
Guoying Liu, Anyang Normal Univ. (China)

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