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Journal of Electronic Imaging

Adaptive classification of hyperspectral images using local consistency
Author(s): Xiaoyong Bian; Xiaolong Zhang; Renfeng Liu; Li Ma; Xiaowei Fu
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Paper Abstract

A spatial method of multistructure sampling based rotation-invariant uniform local binary pattern (named MsLBPriu2) for classification of hyperspectral images is proposed. This method exploits the local property (micro-/macrostructure) of local image patches encoded in the classifier by considering a local neighboring structure around each central pixel and can well suppress the difference of rotational textures for each multicluster class. The proposed method is simple yet efficient for extracting isotropic and anisotropic spatial features from local image patches via different extended sampling on circular regions and elliptical ones with four different rotational angles. Furthermore, the rotation-invariant characteristic of extracted isotropic features is achieved by the inclusion of a rotation-invariant uniform LBP operator. Moreover, the proposed method becomes more robust with respect to the within-class variation. Finally, different classifiers, support vector machine, K-nearest neighbor, and linear discriminant analysis, are compared to evaluate MsLBPriu2 and other feature sets/entropy-based query-by-bagging active learning. We demonstrate the performance of our approach on four different hyperspectral remote sensing images. Experimental results show that the new set of reduced spatial features has a better performance than a variety of state-of-the-art classification algorithms.

Paper Details

Date Published: 15 December 2014
PDF: 17 pages
J. Electron. Imaging. 23(6) 063014 doi: 10.1117/1.JEI.23.6.063014
Published in: Journal of Electronic Imaging Volume 23, Issue 6
Show Author Affiliations
Xiaoyong Bian, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)
Xiaolong Zhang, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)
Renfeng Liu, Huazhong Univ. of Science and Technology (China)
Li Ma, China Univ. of Geosciences (China)
Xiaowei Fu, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)


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