Ground-truth assisted design for remote sensing image classification

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 image show encouraging performances of the proposed framework.