Abstract-This is the pre-print version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure.
In order to better interpret landuses of Synthetic Aperture Radar (SAR) images in urban areas, REF proposes a pseudo-siamese CNN for identifying corresponding patches in very-high-resolution (VHR) optical and SAR remote sensing imagery.
Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters
Mathematics Computer Science Engineering