A sensor-driven domain adaptation method for the classification of remote sensing images

In this paper, a sensor-driven domain adaptation method is proposed for the classification of remote sensing images. The method aims at classifying an image where ground truth is not available exploiting the reference data acquired on a different but related image. This is done by taking advantage from a sensor-driven strategy that exploits the invariance of the measurements of some sensors on some classes for adaptation. This invariant property allows us to infer labels on a subset of unlabeled samples of the image that should be classified, thus introducing constrains on the adaptation process. The proposed method is based on two main steps: i) adaptation based on a sensor-driven label inference method for a subset of classes characterized by spatial invariant behaviour; and ii) adaptation based on machine learning for the remaining classes. The proposed method has been validated on 2 different datasets, where LiDAR data, hyperspectral images and high resolution optical images have been considered.