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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

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Author(s):
dos Santos, Jefersson Alex [1, 2] ; Gosselin, Philippe-Henri [2] ; Philipp-Foliguet, Sylvie [2] ; Torres, Ricardo da S. [1] ; Falcao, Alexandre Xavier [1]
Total Authors: 5
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Univ Cergy Pontoise, ENSEA, CNRS, ETIS, Cergy Pontoise - France
Total Affiliations: 2
Document type: Journal article
Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING; v. 6, n. 4, p. 2020-2034, AUG 2013.
Web of Science Citations: 18
Abstract

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)

FAPESP's process: 09/18438-7 - Large-scale classification and retrieval for complex data
Grantee:Ricardo da Silva Torres
Support type: Regular Research Grants
FAPESP's process: 07/52015-0 - Approximation methods for visual computing
Grantee:Jorge Stolfi
Support type: Research Projects - Thematic Grants