Benchmarking classification of Earth-observation data: from learning explicit features to convolutional networks


Talk at IGARSS’15 special session for the awardees of the Data Fusion Contest, located in Milano Palazzo dei Congressi, Italy.

Abstract: In this paper, we address the task of semantic labeling of multisource earth-observation (EO) data. Precisely, we benchmark several concurrent methods of the last 15 years, from expert classifiers, spectral support-vector classification and high-level features to deep neural networks. We establish that (1) combining multisensor features is essential for retrieving some specific classes, (2) in the image domain, deep convolutional networks obtain significantly better overall performances and (3) transfer of learning from large generic-purpose image sets is highly effective to build EO data classifiers

[ associated paper Award in the Data Fusion Contest 2015 link ]