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The Scientific World Journal
Volume 2013, Article ID 192982, 7 pages
http://dx.doi.org/10.1155/2013/192982
Research Article

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2University of Chinese Academy of Sciences, Beijing 100049, China

Received 24 September 2013; Accepted 19 November 2013

Academic Editors: Z. Hou and R. D. J. Romero-Troncoso

Copyright © 2013 Guizhou Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.