Contextual unsupervised classification of remotely sensed imagery with mixels

Shuji Kawaguchi; Ryuei Nishii

doi:10.1117/12.689566

29 September 2006 Contextual unsupervised classification of remotely sensed imagery with mixels

Shuji Kawaguchi, Ryuei Nishii

Proceedings Volume 6365, Image and Signal Processing for Remote Sensing XII; 63650R (2006) https://doi.org/10.1117/12.689566
Event: SPIE Remote Sensing, 2006, Stockholm, Sweden

Abstract

We propose a contextual unsupervised classification method of geostatistical data based on combination of Ward clustering method and Markov random fields (MRF). Image is clustered into classes by using not only spectrum of pixels but also spatial information. For the classification of remote sensing data of low spatial resolution, the treatment of mixed pixel is importance. From the knowledge that the most of mixed pixels locate in boundaries of land-covers, we first detect edge pixels and remove them from the image. We here introduce a new measure of spatial adjacency of the classes. Spatial adjacency is used to MRF-based update of the classes. Clustering of edge pixels are processed as final step. It is shown that the proposed method gives higher accuracy than conventional clustering method does.

Citation Download Citation

Shuji Kawaguchi and Ryuei Nishii "Contextual unsupervised classification of remotely sensed imagery with mixels", Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650R (29 September 2006); https://doi.org/10.1117/12.689566

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