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Adaptive classification of hyperspectral images using local consistency

[+] Author Affiliations
Xiaoyong Bian

Wuhan University of Science and Technology, School of Computer Science and Technology, Wuhan 430065, China

Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China

Xiaolong Zhang

Wuhan University of Science and Technology, School of Computer Science and Technology, Wuhan 430065, China

Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China

Renfeng Liu

Huazhong University of Science and Technology, School of Automation, Science and Technology on Multi-Spectral Information Processing Laboratory, Wuhan 430074, China

Li Ma

China University of Geosciences, Faculty of Mechanical and Electronic Information Engineering, Wuhan 430074, China

Xiaowei Fu

Wuhan University of Science and Technology, School of Computer Science and Technology, Wuhan 430065, China

Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China

J. Electron. Imaging. 23(6), 063014 (Dec 15, 2014). doi:10.1117/1.JEI.23.6.063014
History: Received July 31, 2014; Accepted November 6, 2014
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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.

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Citation

Xiaoyong Bian ; Xiaolong Zhang ; Renfeng Liu ; Li Ma and Xiaowei Fu
"Adaptive classification of hyperspectral images using local consistency", J. Electron. Imaging. 23(6), 063014 (Dec 15, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.063014


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