A segment-based unsupervised classification for multispectral image

Authors:
Nana Liu;Jingwen Lia;Ning Li
Affiliations:
School of Electronics and Information Engineering, Beihang University, HaiDian District, Beijing, China;School of Electronics and Information Engineering, Beihang University, HaiDian District, Beijing, China;School of Electronics and Information Engineering, Beihang University, HaiDian District, Beijing, China
Venue:
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Year:
2008

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Abstract

We present an unsupervised approach for multispectral 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 vectors of per pixel, and segment it into groups of sub-regions using the graph-based algorithm. Then the fuzzy c-means classifier is used to obtain the classification map based on the sub-regions. Experiments turn out that the approach proposed in this paper can achieve higher accuracy and efficiency by comparing the result with pixel-based fuzzy c-means classification.