Logit-based Superpixel Semantic Segmentation of Images for Precision Agriculture

Abstract

In this work we approach the problem of remote sensing image segmentation using a classical approach: the image is first segmented and, subsequently, each segment is labeled using a classifier. For segmentation, we rely on a superpixel framework and several methods are evaluated. For the classifier, again, several state-of-the-art algorithms are tested and performances are compared. The best performing method is obtained by a modified SEED superpixel algorithm with boosted trees for classification. The evaluation is carried out on the Agriculture-Vision database and the results are encouraging.