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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

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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]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Univ Cergy Pontoise, ENSEA, CNRS, ETIS, Cergy Pontoise - France
Número total de Afiliações: 2
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: 08/58528-2 - Classificação semi-automática de regiões em imagens de sensoriamento remoto utilizando Realimentação de Relevância
Beneficiário:Jefersson Alex dos Santos
Linha de fomento: Bolsas no Brasil - Doutorado
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: 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: 07/52015-0 - Métodos de aproximação para computação visual
Beneficiário:Jorge Stolfi
Linha de fomento: Auxílio à Pesquisa - Temático