Interactive Design of Object Classifiers in Remote Sensing

This paper deals with the interactive design of generic classifiers for aerial images. In many real-life cases, object detectors that work are not available, due to a new geographical context or a need for a type of object unseen before. We propose an approach for on-line learning of such detectors using user interactions. Variants of gradient boosting and support-vector machine classification are proposed to cope with the problems raised by interactivity: unbalanced and partially mislabeled training data. We assess our framework for various visual classes (buildings, vegetation, cars, visual changes) on challenging data corresponding to several applications (SAR or optical sensors at various resolutions). We show that our model and algorithms outperform several state-of-the-art baselines for feature extraction and learning in remote sensing.