Projects

Interactive Annotation of Remote Sensing Images

Medium remote sensing

Remote sensing image classification is a challenging and basic task to verify land cover and plan land use. Our strategy has been to segment the image into superpixels for contextual characterization, data organization, and active learning. Given the large size of remote sensing images for expert supervision, we have developed efficient user interaction mechanisms for active learning in order to design a classifier with minimum effort and time of the specialist. In addition to the development and validation of the methods by real specialists (i.e., with no simulation of their actions), we aim at a realistic solution by also treating non-interest regions for image annotation.

Team:

Alexandre Xavier Falc√£o
Devis Tuia
Jeferson Alex dos Santos
John Edgar Vargas