[LLB15] Benchmarking classification of earth-observation data: from learning explicit features to convolutional networks

Conférence Internationale avec comité de lecture : IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), July 2015, pp.-, Italie,

Auteurs: A. Lagrange , B. Le Saux , A. Beaupere , A. Boulch , A. Chan-Hon-Tong , S. Herbin , H. Randrianarivo , M. Ferecatu

Mots clés: Remote sensing, Image classification, Pattern analysis, Neural networks

Résumé: 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.

Equipe: vertigo

Collaboration: , ONERA - DTIM , ONERA

BibTeX

@inproceedings {
	LLB15,
	title	=	"{Benchmarking classification of earth-observation data: from learning explicit features to convolutional networks}",
	author	=	" A. Lagrange and B. Le Saux and A. Beaupere and A. Boulch and A. Chan-Hon-Tong and S. Herbin and H. Randrianarivo and M. Ferecatu ",
	booktitle	=	"{IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015)}",
	year	=	2015,
	month	=	"July",
	pages	=	"-",
	address	=	" Italie",
}