Reinforcement-based frugal learning for satellite image change detection
This addresses satellite image analysis for remote sensing applications, presenting an incremental improvement through active learning integration.
The paper tackles satellite image change detection by developing an interactive algorithm that queries users about changes and updates detections based on responses, using a probabilistic framework with relevance measures. Experiments show this reinforcement learning approach improves generalization in change detection tasks.
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.