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Proceedings Paper

Multi-feature object identification of remote sensing image based on vague soft sets
Author(s): Bo Wei; Zhichao Wang; Qingqing Xie; Kailin Zhang
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Paper Abstract

Multi-feature classification and image segmentation are two cores in object-oriented classification method of high resolution remote sensing images. Multi-feature object identification is an important part of multi-feature classification, which is identification for the image regions or the segmentation objects segmented by image segmentation under the guidance of a corresponding relationship between objects and features or combination. A method of multi-feature object identification was proposed based on vague soft sets. Firstly, the vague soft sets were formed by building the parameter sets according to spectral characteristics and object-oriented features of the segmentation objects. Secondly, according to general TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution), a TOPSIS based on Vague soft sets for multi-feature object identification was proposed, which obtained a object identification result of the segmentation objects by using similarity measure of vague soft sets to sort attribution of the cover types for the segmentation objects. The experimental results show that the proposed method obtains a correct result of object identification and is feasible and effective.

Paper Details

Date Published: 9 December 2015
PDF: 12 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98080C (9 December 2015); doi: 10.1117/12.2207420
Show Author Affiliations
Bo Wei, Guilin Univ. of Technology (China)
Zhichao Wang, Guilin Univ. of Technology (China)
Qingqing Xie, Guilin Univ. of Technology (China)
Kailin Zhang, Guilin Univ. of Technology (China)

Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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