Domain adaptation in remote sensing through cross-image synthesis with dictionaries

This contribution studies an approach based on dictionary learning which enables the alignment of the sparse representations of two images. Set in a domain adaptation context, the purpose of this work is to re-synthesize the pixels of a remote sensing image so that, for a given land-cover class, the new values of the samples are comparable across acquisitions. Consequently, the data space of a given source image can be converted to that of a related target image, or vice-versa. After the mentioned transformation, the performance of a classifier trained on the source image and used to predict the thematic classes on the target image is expected to be more robust. A linear transformation is derived thanks to an algorithm simultaneously learning the image-specific dictionaries and the mapping function bridging them via their respective sparse codes. Experiments on knowledge transfer among two co-registered VHR images acquired with different off-nadir angles show promising results. An appropriate cross-image synthesis yields an increased land-cover model portability from one acquisition to another.