SAR segmentation by a two-scale contextual classifier

Markku Similae

doi:10.1117/12.196742

30 December 1994 SAR segmentation by a two-scale contextual classifier

Markku Similae

Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196742
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

In this paper the SAR image segmentation problem, which here is identified with the classification task, is discussed in the following situation. The intensity level represents only a vague information about the ground truth class although it is not totally uninformative. We assume, however, that it is possible to extract meaningful textural features from the image, e.g. often it is natural to assume that the ground truth classes have a different dependence structure which in turn implies that one meaningful feature is the autocorrelation. The segmentation problem is formulated as a posterior distribution maximization. Under these conditions the informative value of the intensity is low. So we restrict the configuration space over which the maximization takes place by conditioning on the textural features. The textural features segment the image crudely. This segmentation is called a larger scale classification. In many cases it is possible to refine this segmentation in a smaller pixelwise scale using the intensity values. The uncertainty relating to the larger scale segmentation is passed to the pixelwise classifier as a distribution of the classes. This distribution is then combined with the spatial prior. The final step in the segmentation is the use of ICM algorithm by Besag to achieve the desired classification map. This approach to the segmentation is motivated by the problems confronted in the sea ice/open water classification from ERS-1 SAR images. Hence all the examples are from this application.

Citation Download Citation

Markku Similae "SAR segmentation by a two-scale contextual classifier", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196742

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