Reinforcement-Based Frugal Learning for Interactive Satellite Image Change Detection
Sebastien Deschamps, Hichem Sahbi · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
In this paper, we introduce a novel interactive satellite im-age change detection algorithm based on active learning. The proposed approach is iterative and asks the user (oracle) questions about the targeted changes and according to the oracle's responses updates change detections. We consider a proba-bilistic framework which assigns to each unlabeled sample a relevance measure modeling how critical is that sample when training change detection functions. These relevance mea-sures are obtained by minimizing an objective function mixing diversity, representativity and uncertainty. These criteria when combined allow exploring different data modes and also refining change detections. To further explore the potential of this objective function, we consider a reinforcement learning approach that finds the best combination of diversity, repre-sentativity and uncertainty, through active learning iterations, leading to better generalization as corroborated through ex-periments in interactive satellite image change detection.