Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

Dynamic classifier selection using spectral-spatial information for hyperspectral image classification

[+] Author Affiliations
Hongjun Su

Hohai University, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, China

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210046, China

Bin Yong

Hohai University, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, China

Peijun Du

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210046, China

Hao Liu

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

Chen Chen

University of Texas at Dallas, Department of Electrical Engineering, Richardson, Texas 75080-3021, United States

Kui Liu

University of Texas at Dallas, Department of Electrical Engineering, Richardson, Texas 75080-3021, United States

J. Appl. Remote Sens. 8(1), 085095 (Aug 22, 2014). doi:10.1117/1.JRS.8.085095
History: Received March 9, 2014; Revised July 9, 2014; Accepted July 15, 2014
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Abstract.  This paper presents a dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel’s label once the remaining classified pixels’ neighborhood meets the threshold. For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a minimum estimated abundance covariance-based band selection is used. Two hyperspectral remote sensing datasets, HYDICE Washington DC Mall and AVIRIS Indian Pines, are employed to evaluate the performance of the developed method. The classification accuracies of the two datasets are improved by 1.13% and 4.47%, respectively, compared with the traditional algorithms using spectral information. The experimental results demonstrate that the integration of spectral information with volumetric textural features can improve the classification performance for hyperspectral images.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Hongjun Su ; Bin Yong ; Peijun Du ; Hao Liu ; Chen Chen, et al.
"Dynamic classifier selection using spectral-spatial information for hyperspectral image classification", J. Appl. Remote Sens. 8(1), 085095 (Aug 22, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085095


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