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• W2470802587 abstract "Morphological operators (MOs) and their enhancementssuch as morphological profiles (MPs) are subject to a livelyscientific contemplation since they are found to be beneficial for,for example, classification of very high spatial resolution panchromatic,multi-, and hyperspectral imagery. They account for spatialstructures with differing magnitudes and, thus, provide a comprehensivemultilevel description of an image. In this paper, weintroduce the concept of object-based MPs (OMPs) to also encodeshape-related, topological, and hierarchical properties of imageobjects in an exhaustive way. Thereby, we seek to benefit from theso-called object-based image analysis framework by partitioningthe original image into objects with a segmentation algorithm onmultiple scales. The obtained spatial entities (i.e., objects) are usedto aggregate multiple sequences obtained with MOs according tostatistical measures of central tendency. This strategy is followedto simultaneously preserve and characterize shape properties ofobjects and enable both the topological and hierarchical decompositionsof an image with respect to the progressive application ofMOs. Subsequently, supervised classification models are learnedby considering this additionally encoded information. Experimentalresults are obtained with a random forest classifier withheuristically tuned hyperparameters and a wrapper-based featureselection scheme. We evaluated the results for two test sites ofpanchromatic WorldView-II imagery, which was acquired over anurban environment. In this setting, the proposed OMPs allow forsignificant improvements with respect to classification accuracycompared to standard MPs (i.e., obtained by paired sequences oferosion, dilation, opening, closing, opening by top-hat, and closingby top-hat operations)." @default.
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• W2470802587 date "2016-10-01" @default.
• W2470802587 modified "2023-09-23" @default.
• W2470802587 title "Object-Based Morphological Profiles for Classification of Remote Sensing Imagery" @default.
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• W2470802587 doi "https://doi.org/10.1109/tgrs.2016.2576978" @default.
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