Special Section on Remote Sensing for Coupled Natural Systems and Built Environments

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

[+] Author Affiliations
He Yang, Ben Ma, Qian Du

Mississippi State University, Department of Electrical and Computer Engineering, Geosystems Research Institute, Mississippi State, Mississippi 39762

Chenghai Yang

United Stated Department of Agriculture, Agricultural Research Service, Kika de la Garza Subtropical Agricultural Research Center, Weslaco, Texas 78596

J. Appl. Remote Sens. 4(1), 041890 (August 31, 2010). doi:10.1117/1.3491192
History: Received March 30, 2010; Revised May 18, 2010; Accepted June 4, 2010; August 31, 2010; Online August 31, 2010
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Abstract

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

© 2010 Society of Photo-Optical Instrumentation Engineers

Citation

He Yang ; Ben Ma ; Qian Du and Chenghai Yang
"Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information", J. Appl. Remote Sens. 4(1), 041890 (August 31, 2010). ; http://dx.doi.org/10.1117/1.3491192


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