Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

Unsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral remotely sensed imagery

[+] Author Affiliations
Xiong Xu

Tongji University, College of Surveying and Geo-Informatics, 1239 Siping Road, Shanghai 200092, China

Xiaohua Tong

Tongji University, College of Surveying and Geo-Informatics, 1239 Siping Road, Shanghai 200092, China

Liangpei Zhang

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, 129 Luoyu Road, Wuhan, Hubei 430079, China

Hongzan Jiao

Wuhan University, School of Urban Design, 129 Luoyu Road, Wuhan, Hubei 430079, China

Huan Xie

Tongji University, College of Surveying and Geo-Informatics, 1239 Siping Road, Shanghai 200092, China

J. Appl. Remote Sens. 8(1), 085090 (Oct 03, 2014). doi:10.1117/1.JRS.8.085090
History: Received May 6, 2014; Revised August 24, 2014; Accepted September 4, 2014
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Abstract.  Remote sensing has become an important source of urban land-use/cover classification, and as a result of their high spatial and spectral resolution, airborne hyperspectral images have been widely used to distinguish different urban classes. However, the previous studies into the classification of urban environments have mainly focused on a supervised scenario, which is limited by the selection of training samples. An unsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral imagery is proposed by a combination of endmember extraction and k-means classification. The number of endmembers of the hyperspectral image is first estimated with the hyperspectral signal subspace identification with the minimum error method, and then the simplex growing algorithm is used to extract the endmember spectra that represent the different latent materials in the hyperspectral imagery. These latent materials were further integrated into the predefined number of classes and the k-means classification method was utilized to obtain the final classification map. Different distance measures were experimentally used in the procedure of class integration and classification to investigate the impact of initial cluster centers and further clustering criterion. The proposed strategy was compared with three traditional unsupervised classification methods, k-means, fuzzy k-means, and ISODATA, with two airborne hyperspectral images. The experimental results demonstrate that the proposed approach is more robust and outperforms the other three classification methods, and, hence, provides an innovative perspective for implementing the unsupervised classification of airborne hyperspectral imagery.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Xiong Xu ; Xiaohua Tong ; Liangpei Zhang ; Hongzan Jiao and Huan Xie
"Unsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral remotely sensed imagery", J. Appl. Remote Sens. 8(1), 085090 (Oct 03, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085090


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