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Proceedings Paper

A class-oriented model for hyperspectral image classification through hierarchy-tree-based selection
Author(s): Zhongqi Tang; Guangyuan Fu; XiaoLin Zhao; Jin Chen; Li Zhang
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Paper Abstract

With the development of hyperspectral sensors over the last few decades, hyperspectral images (HSIs) face new challenges in the field of data analysis. Due to those high-dimensional data, the most challenging issue is to select an effective yet minimal subset from a mass of bands. This paper proposes a class-oriented model to solve the task of classification by incorporating spectral prior of the target, since different targets have different characteristics in spectral correlation. This model operates feature selection after a partition of hyperspectral data into groups along the spectral dimension. In the process of spectral partition, we group the raw data into several subsets by a hierarchy tree structure. In each group, band selection is performed via a recursive support vector machine (R-SVM) learning, which reduces the computational cost as well as preserves the accuracy of classification. To ensure the robustness of the result, we also present a weight-voting strategy for result merging, in which the spectral independency and the classification effectivity are both considered. Extensive experiments show that our model achieves better performance than the existing methods in task-dependent classifications, such as target detection and identification.

Paper Details

Date Published: 2 March 2016
PDF: 6 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990117 (2 March 2016); doi: 10.1117/12.2234792
Show Author Affiliations
Zhongqi Tang, Xi'an Institute of High Technology (China)
Tsinghua Univ. (China)
Guangyuan Fu, Xi'an Institute of High Technology (China)
XiaoLin Zhao, Air Force Engineering Univ. (China)
Jin Chen, Beijing Institute of Remote Sensing Information (China)
Li Zhang, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

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