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• W2751609968 abstract "In this article we present two different approaches for automatic remote sensing image interpretation which are based on a multi-paradigm collaborative framework which uses classification in order to guide the segmentation process. The first approach applies sequentially many one-vs-all class extractors in a manner inspired by cascading techniques in machine learning. The second approach applies many collaborating one-vs-all class extractors in parallel. We show that the collaboration of the segmentation and classification paradigms result in a remarkable reduction of segmentation errors but also in better object classification in comparison to a hybrid pixel-object approach as well as a deep learning approach." @default.
• W2751609968 created "2017-09-15" @default.
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• W2751609968 date "2018-01-01" @default.
• W2751609968 modified "2023-10-16" @default.
• W2751609968 title "Remote sensing image analysis by aggregation of segmentation-classification collaborative agents" @default.
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• W2751609968 doi "https://doi.org/10.1016/j.patcog.2017.08.030" @default.
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