|YANG, HE - Mississippi State University|
|MA, BEN - Mississippi State University|
|DU, QIAN - Mississippi State University|
Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/24/2010
Publication Date: 12/30/2010
Citation: Yang, H., Ma, B., Du, Q., Yang, C. 2010. Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information. Journal of Applied Remote Sensing. 4:041890.
Interpretive Summary: Urban areas are of great interest to researchers and practitioners on planning and environmental management. This study proposed approaches to improve an image classification method, called pixel-based support vector machine (SVM), for urban land use and land cover mapping from high resolution airborne hyperspectral imagery. These approaches are based on class spatial neighborhood relationship and pixel connectivity. Experimental results demonstrate the proposed approaches significantly improve classification accuracy for urban land monitoring and planning.
Technical 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.