Multiscale Classification of Remote Sensing Images

Jefersson Alex dos Santos (1) , Philippe-Henri Gosselin (2) , Sylvie Philipp-Foliguet (3) , Ricardo da Silva Torres (1) , Alexandre Xavier Falcao (1)

1 RECOD - REasoning for Complex Data
2 MIDI - Multimedia Indexation and Data Integration
3 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes

A huge effort has been applied in image classification to create high quality thematic maps and to establish precise inventories about land cover use. The peculiarities of Remote Sensing Images (RSIs) combined with the traditional image classification challenges made RSIs classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multi-scale segmentation. We use the paradigm of boosting, whose principle is to combine weak classifiers to build an efficient global one. Each weak classifier is trained for one level of the segmentation and one region descriptor. We have proposed and tested weak classifiers based on linear SVM and region distances provided by descriptors. The experiments were performed on a large image of coffee plantations. We have shown in this paper that our approach based on boosting can detect the scale and set of features best suited to a particular training set. We have also shown that hierarchical multi-scale analysis is able to reduce training time and to produce a stronger classifier. We compare the proposed methods with a baseline based on SVM with RBF kernel. The results show that the proposed methods outperform the baseline.

Domains: Machine Learning [stat.ML]; Multimedia [cs.MM]

Jefersson Alex dos Santos, Philippe-Henri Gosselin, Sylvie Philipp-Foliguet, Ricardo da Silva Torres, Alexandre Xavier Falcao. Multiscale Classification of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50 (10), pp.3764-3775. ⟨hal-00753160⟩