Reinforcement-based frugal learning for satellite image change detection

TL;DR

This paper presents a reinforcement learning-based active learning method for satellite image change detection, which iteratively interacts with users to improve detection accuracy by optimizing sample relevance measures.

Contribution

It introduces a novel reinforcement learning framework that optimally combines diversity, representativity, and uncertainty in active learning for satellite image change detection.

Findings

Improved change detection accuracy through reinforcement learning optimization.

Effective exploration of data modes via combined relevance criteria.

Validation on satellite images shows enhanced generalization.

Abstract

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.