Abstract
Recursive tree-structured segmentation is a powerful tool to deal with the non-stationary nature of images. By fitting model parameters to each region/class under analysis one can adapt the segmentation algorithm to the local image statistics, thus improving accuracy. However, a single model/segmenter cannot fit regions with wildly different nature, and one should be allowed to select in a suitable library the tool most suited to the local statistics. In this paper, we implement this dynamic segmentation/classification paradigm, using two segmenters, based on spectral and textural properties, respectively, and defining suitable rules for switching model locally. Experiments on remote-sensing mosaics show that the multiple-model dynamic algorithm largely outperforms comparable single-model segmenters.
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Scarpa, G., Masi, G., Gaetano, R., Verdoliva, L., Poggi, G. (2013). Dynamic Hierarchical Segmentation of Remote Sensing Images. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_38
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DOI: https://doi.org/10.1007/978-3-642-41181-6_38
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