ASSESSING THE EFFECTIVENESS OF INPAINTING TECHNIQUES FOR ENHANCING FEATURE EXTRACTION QUALITY IN REMOTE SENSING IMAGERY

Abstract

Remote Sensing (RS) images have been used in several applications of interest for society. Despite the precision and robustness derived from RS images, several aerial scenes exhibit imperfections and fall short of attaining ideal quality standards, as some of them present distortions such as noise, blur, and stripes. An alternative approach to deal with such distortions is by applying Inpainting techniques, however, under certain circumstances, this type of approach requires to be evaluated by quantitative metrics to assess the final quality of the reconstruction. Therefore, this paper focus on the issue of quantitatively evaluating inpainting results in the context of RS by analysing and comparing new evaluation metrics in contrast to the classical ones from the general literature of RS. More precisely, two inpainting techniques are applied for object removal and reconstruction of partially detected curvilinear cartographic features in RS images. Next, the obtained results are evaluated by taking six evaluation metrics to assess the agreement level between the metrics, as well as between qualitative evaluations conducted by human agents. Based on the evaluation of these metrics when applied to RS images, it can be concluded that the DISTS and VSI metrics are the most promising candidates for adaptation and application within the specific context of RS.