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End-to-End Learning of Polygons for Remote Sensing Image Classification

Abstract : While geographic information systems typically use polygonal representations to map Earth's objects, most state-of-the-art methods produce maps by performing pixelwise classification of remote sensing images, then vectorizing the outputs. This paper studies if one can learn to directly output a vectorial semantic labeling of the image. We here cast a mapping problem as a polygon prediction task, and propose a deep learning approach which predicts vertices of the polygons outlining objects of interest. Experimental results on the Solar photovoltaic array location dataset show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs.
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Contributor : Nicolas Girard Connect in order to contact the contributor
Submitted on : Tuesday, April 10, 2018 - 10:11:31 AM
Last modification on : Monday, October 12, 2020 - 10:28:55 AM


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  • HAL Id : hal-01762446, version 1



Nicolas Girard, Yuliya Tarabalka. End-to-End Learning of Polygons for Remote Sensing Image Classification. IEEE International Geoscience and Remote Sensing Symposium – IGARSS 2018, Jul 2018, Valencia, Spain. ⟨hal-01762446⟩



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