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

He Yang; Ben Ma; Qian Du; Chenghai Yang

doi:10.1117/1.3491192

1 August 2010 Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

He Yang, Ben Ma, Qian Du, Chenghai Yang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 4, Issue 1, 041890 (August 2010). https://doi.org/10.1117/1.3491192

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.

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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," Journal of Applied Remote Sensing 4(1), 041890 (1 August 2010). https://doi.org/10.1117/1.3491192

Published: 1 August 2010

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