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Proceedings Paper

Remote sensing image classification based on support vector machine with the multi-scale segmentation
Author(s): Wenxing Bao; Wei Feng; Ruishi Ma
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Paper Abstract

In this paper, we proposed a new classification method based on support vector machine (SVM) combined with multi-scale segmentation. The proposed method obtains satisfactory segmentation results which are based on both the spectral characteristics and the shape parameters of segments. SVM method is used to label all these regions after multiscale segmentation. It can effectively improve the classification results. Firstly, the homogeneity of the object spectra, texture and shape are calculated from the input image. Secondly, multi-scale segmentation method is applied to the RS image. Combining graph theory based optimization with the multi-scale image segmentations, the resulting segments are merged regarding the heterogeneity criteria. Finally, based on the segmentation result, the model of SVM combined with spectrum texture classification is constructed and applied. The results show that the proposed method can effectively improve the remote sensing image classification accuracy and classification efficiency.

Paper Details

Date Published: 9 December 2015
PDF: 7 pages
Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98171C (9 December 2015); doi: 10.1117/12.2228099
Show Author Affiliations
Wenxing Bao, Beifang Univ. of Nationalities (China)
Wei Feng, Bordeaux INP (France)
Ruishi Ma, Beifang Univ. of Nationalities (China)

Published in SPIE Proceedings Vol. 9817:
Seventh International Conference on Graphic and Image Processing (ICGIP 2015)
Yulin Wang; Xudong Jiang, Editor(s)

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