Interactive Multiscale Classification of High-Resolution Remote Sensing Images

Autor(es)

dos Santos, Jefersson Alex ^{[1, 2]} ; Gosselin, Philippe-Henri ^[2] ; Philipp-Foliguet, Sylvie ^[2] ; Torres, Ricardo da S. ^[1] ; Falcao, Alexandre Xavier ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil

^[2] Univ Cergy Pontoise, ENSEA, CNRS, ETIS, Cergy Pontoise - France

• Tipo de documento: Artigo Científico
• Fonte: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING; v. 6, n. 4, p. 2020-2034, AUG 2013.
• Citações Web of Science: 18

Resumo

The use of remote sensing images (RSIs) as a source of information in agribusiness applications is very common. In those applications, it is fundamental to identify and understand trends and patterns in space occupation. However, the identification and recognition of crop regions in remote sensing images are not trivial tasks yet. In high-resolution image analysis and recognition, many of the problems are related to the representation scale of the data, and to both the size and the representativeness of the training set. In this paper, we propose a method for interactive classification of remote sensing images considering multiscale segmentation. Our aim is to improve the selection of training samples using the features from the most appropriate scales of representation. We use a boosting-based active learning strategy to select regions at various scales for user's relevance feedback. The idea is to select the regions that are closer to the border that separates both target classes: relevant and non-relevant regions. Experimental results showed that the combination of scales produces better results than isolated scales in a relevance feedback process. Furthermore, the interactive method achieved good results with few user interactions. The proposed method needs only a small portion of the training set to build classifiers that are as strong as the ones generated by a supervised method that uses the whole training set. (AU)

• Processo FAPESP: 08/57428-4 - Automatização do diagnóstico de parasitos intestinais do homem por análise de imagens
• Beneficiário: Alexandre Xavier Falcão
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 07/52015-0 - Métodos de aproximação para computação visual
• Beneficiário: Jorge Stolfi
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 09/18438-7 - Classificação e busca em grande escala para dados complexos
• Beneficiário: Ricardo da Silva Torres
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 08/58528-2 - Classificacao semi-automatica de regioes em imagens de sensoriamento remoto utilizando realimentacao de relevancia
• Beneficiário: Jefersson Alex dos Santos
• Linha de fomento: Bolsas no Brasil - Doutorado