Skip to Main content Skip to Navigation
Conference papers

Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images

Abstract : 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.
Document type :
Conference papers
Complete list of metadata
Contributor : Armelle Sielinou Connect in order to contact the contributor
Submitted on : Tuesday, July 11, 2017 - 5:53:50 PM
Last modification on : Tuesday, June 15, 2021 - 2:57:01 PM



Jérémie Sublime, Antoine Cornuéjols, Younès Bennani. Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images. International Conference on Neural Information (ICONIP) Processing, Oct 2016, Tokyo, Japan. pp.148-155, ⟨10.1007/978-3-319-46675-0_17⟩. ⟨hal-01560656⟩



Les métriques sont temporairement indisponibles