REINFORCEMENT-BASED FRUGAL LEARNING FOR INTERACTIVE SATELLITE IMAGE CHANGE DETECTION

Sébastien Deschamps (1) , Hichem Sahbi (1)

1 SYEL - Systèmes Electroniques

In this paper, we introduce a novel interactive satellite image 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 probabilistic framework which assigns to each unlabeled sample a relevance measure modeling how critical is that sample when training change detection functions. These relevance measures 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, representativity and uncertainty, through active learning iterations, leading to better generalization as corroborated through experiments in interactive satellite image change detection.

Mots clés: active learning; reinforcement learning; satellite image change detection

Domaines: Intelligence artificielle [cs.AI]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Sébastien Deschamps, Hichem Sahbi. REINFORCEMENT-BASED FRUGAL LEARNING FOR INTERACTIVE SATELLITE IMAGE CHANGE DETECTION. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2022, Kuala Lumpur, Malaysia. pp.627-630, ⟨10.1109/IGARSS46834.2022.9883633⟩. ⟨hal-03838835⟩