Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images
In this article, we show an application of collaborative clustering applied to real data from very high resolution images. Our proposed method makes it possible to have several algorithms working at different scales of details while exchanging their information on the clusters.
Our method that aims at strengthening the hierarchical links between the clusters extracted at different level of detail has shown good results in terms of clustering quality based on common unsupervised learning indexes, but also when using external indexes: We compared our results with other algorithms and analyzed them based on an expert ground truth.
KeywordsMulti-scale clustering Cluster analysis Image segmentation
This work has been supported by the ANR Project COCLICO, ANR-12-MONU-0001.
- 3.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2006)Google Scholar
- 4.Grozavu, N., Bennani, Y.: Topological collaborative clustering. Aust. J. Intell. Inf. Proces. Syst. 12(3) (2010). https://cs.anu.edu.au/ojs/index.php/ajiips/issue/view/156
- 8.Rougier, S., Puissant, A.: Improvements of urban vegetation segmentation and classification using multi-temporal pleiades images. In: 5th International Conference on Geographic Object-Based Image Analysis, p. 6 (2014)Google Scholar
- 9.Sublime, J., Troya-Galvis, A., Bennani, Y., Gancarski, P., Cornuéjols, A.: Semantic rich ICM algorithm for VHR satellite image segmentation. In: IAPR International Conference on Machine Vision Applications, Tokyo (2015)Google Scholar