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In the remote sensing field, ground-truth design for collecting training samples represents a tricky and critical problem since it has a direct impact on most of the subsequent image processing and analysis steps. In this paper, we propose a novel framework for assisting a human user in designing ground-truth by photointerpretation for optical remote sensing image classification. The proposed approach...
In this work, we propose a framework to help in the design of the ground-truth for the classification of remote sensing images. It consists first to segment the considered image by means of a level set method and then to extract the segments characterized by the largest numbers of pixels. Afterward, the selected segments are labeled by a human user. Experimental results obtained on a very high resolution...
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