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Automated interpretation of remotely sensed data poses certain demands to image segmentation algorithms, regarding speed, memory requirements, segmentation quality, noise robustness, complexity, and reproducibility. This paper addresses these issues by formulating image segmentation as source channel coding with side information. A cost function is developed that approximates the expected code length for a hypothetical two-part coding scheme. The cost function combines region-based and edge-based considerations, and it supports the utilization of reference data to enhance segmentation results. Optimization is implemented by an agglomerative segmentation algorithm that iteratively creates a tree-like description of the image. Given a fixed tree level and the output of the edge detector, the cost function is parameter-free, so that no exhaustive parameter-tuning is necessary. Additionally, a criterion is presented to reliably select an adequate tree level with high descriptive quality. It is shown by statistical analysis that the cost function is appropriate for both multispectral and synthetic aperture radar data. Experimental results confirm the high quality of the resulting segmentations.