A Genetic Automatic Ground-Truth Validation Method for Multispectral Remote Sensing Images

In this paper, we propose a novel genetic method that aims at providing the ground-truth expert with a binary information of the kind "validated"/"invalidated" for each ground-truth (learning) sample collected. For each invalidated sample, the expert may confirm or not the invalidation, and thus correct or maintain the adopted labeling before creating the final learning set that will be exploited in the classification process. Experimental results confirm the effectiveness of the proposed method in correctly detecting mislabeled learning samples and thus in limiting their negative impact on the classification process.