Consensual clustering for land cover mapping

In this article we propose to illustrate the ability of consensual clustering to provide mining tools in the context of land cover unsupervised classification. The proposed algorithm is based on individual co-association matrices related to several input clusterings that are combined using a Mean Shift optimization procedure. This provides valuable clusters in terms of interpretation and also information about the data to be clustered, which could be useful to discriminate between easily classified pixels and the other ones, requiring human expertise. The interest of our approach is demonstrated using the Boumerdes dataset provided by SERTIT and CNES, in the context of the 2003 earthquake.