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In this letter, we propose a pseudo-siamese convolutional neural network architecture that enables to solve the task of identifying corresponding patches in very high-resolution 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 data set that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently coregistered 3-D point clouds. The satellite images, from which the patches comprising our data set 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 toward a generalized multisensor key-point matching procedure.

Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN