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Proceedings Paper

Remotely sensed imagery intelligent interpretation based on image segmentation and support vector machines
Author(s): Dengkui Mo; Hui Lin; Jiping Li; Hua Sun; Tailong Liu; Yujiu Xiong
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Paper Abstract

Remote sensing provides a useful source of data from which updated land cover information can be extraction for assessing and monitoring environment changes. This paper aims at achieving improved land cover classification performance based image segmentation and support vector machines (SVMs) classification. The object-based classification approach overcame the problem of salt-and-pepper effects found in classification results from traditional pixel-based approaches. The proposed method is a three-stage process, which makes use of the object information from neighboring pixels. Firstly, a robust image segmentation algorithm is used to achieve more homogeneous regions. Secondly, feature information is extracted from each segment and training samples is interactive selected in geographical information system platform. Thirdly, support vector machines classifier is employed to classify the land covers. The experimental results indicate that improved classification accuracy and smoother (more acceptable) is achieved compare with the traditional pixel-based method. Because of the image segmentation process significantly reduces the number of training samples, make SVMs classification method can be applied to information extraction from remotely sensed data.

Paper Details

Date Published: 8 August 2007
PDF: 9 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67520N (8 August 2007); doi: 10.1117/12.760450
Show Author Affiliations
Dengkui Mo, Central South Univ. (China)
Hui Lin, Central South Univ. (China)
Jiping Li, Central South Univ. (China)
Hua Sun, Central South Univ. (China)
Tailong Liu, State Forestry Administration (China)
Yujiu Xiong, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)

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