Feature Selection for Satellite Image Indexing

1. Feature Selection for Satellite Image Indexing • Marine Campedel*, Eric Moulines*, Henri Maître* and Mihai Datcu** • Competence Center on Information Extraction and Image Understanding for Earth Observation • *GET-Telecom Paris 46 rue Barrault 75013 Paris – France - e-mail: Marine.Campedel@enst.fr • **German Aerospace Center (DLR) – D-82234 Oberfaffenhaffen – Germany – e-mail : Mihai.Datcu@dlr.de Feature Selection Features (+labels) Cross validation loop Learning/Testing Feature Selection Feature Selection Classification Heuristics Classifier learning Classification error rates (means and variances) Redundancy Evaluation Experiments Features Extraction Features Selection Small Images 64x64 Spot5 (5m/pixel) @CNES 6 semantic classes : city, forest, field, sea, desert, cloud 100 images for each class Classification Classification error rates (means and variances) H representation entropy: high values indicate low redundancy Classification error rates (mean and variances) • Features sets efficiency is compared using automatic feature selection and classification algorithms. We observe that : • Automatic feature selection enhances the classification performances; • Selection and classification algorithms have to be chosen jointly; • Textural and geometrical features are complementary for Spot 5 images • (4/14 Fisher-FS selected features are geometrical).