Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection
This work addresses the challenge of reducing labeling effort for satellite image analysis, which is incremental as it builds on active learning methods.
The paper tackles the problem of label-efficient satellite image change detection by proposing an interactive active learning framework that selects informative virtual exemplars, resulting in superior performance compared to related work.
In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning. The proposed framework is iterative and relies on a question and answer model which asks the oracle (user) questions about the most informative display (subset of critical images), and according to the user's responses, updates change detections. The contribution of our framework resides in a novel display model which selects the most representative and diverse virtual exemplars that adversely challenge the learned change detection functions, thereby leading to highly discriminating functions in the subsequent iterations of active learning. Extensive experiments, conducted on the challenging task of interactive satellite image change detection, show the superiority of the proposed virtual display model against the related work.