A graph-segment-based unsupervised classification for multispectral remote sensing images

Authors:
Nana Liu;Jingwen Li;Ning Li
Affiliations:
School of Electronics and Information Engineering, Beihang University, Beijing, China;School of Electronics and Information Engineering, Beihang University, Beijing, China;State Key Lab of Software Development Environment, Beihang University, Beijing, China
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2008

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Abstract

With more applications of multispectral remote sensing images, how to effectively and correctly make automated classification of multispectral images is still a great challenge. Utilizing both spatial contextual information and spectral information can achieve better classification performance. In order to make better utilization of the spatial contextual information, we apply graph model to the multispectral image, and use graph-based segmentation to produce units of pixels for further classification. In this paper, we present an unsupervised approach for multispectral remote sensing image classification with graph-based segment and fuzzy c-means clustering. Our method mainly involves following steps: First, represent image as graph H = (V,E) based on the feature vector of per pixel and the relationships among neighboring pixels, and segment the graph into groups of sub-regions as basic object units using the effective graph segmentation algorithm. Then according to those global feature vectors of sub-regions, the fuzzy c-means clustering is used to obtain the classification map based on these sub-regions. Experiments shows the results by different segmentation scales, and then turn out that the approach proposed in this paper can achieve better accuracy and efficiency.