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Journal of Applied Remote Sensing

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information
Author(s): He Yang; Ben Ma; Qian Du; Chenghai Yang
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Paper 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.

Paper Details

Date Published: 1 August 2010
PDF: 14 pages
J. Appl. Remote Sens. 4(1) 041890 doi: 10.1117/1.3491192
Published in: Journal of Applied Remote Sensing Volume 4, Issue 1
Show Author Affiliations
He Yang, Mississippi State Univ. (United States)
Ben Ma, Mississippi State Univ. (United States)
Qian Du, Mississippi State Univ. (United States)
Chenghai Yang, Agricultural Research Service (United States)

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