The automatic matching of corresponding pixels in SAR and optical remote sensing imagery has been an active field of research for many years. While early approaches were usually based on the measurement of image similarity by signal-based measures or hand-crafted image features, more recent matching techniques make use of deep learning. Since the different approaches proposed in the literature are usually trained and evaluated on specific, individual datasets, i.e. with unique input data and target label criteria, a direct comparison has not yet been possible. With this paper, we intend to close that gap by providing the first comparative evaluation of different state-of-the-art deep learning-based SAR-optical image matching approaches.    «

The automatic matching of corresponding pixels in SAR and optical remote sensing imagery has been an active field of research for many years. While early approaches were usually based on the measurement of image similarity by signal-based measures or hand-crafted image features, more recent matching techniques make use of deep learning. Since the different approaches proposed in the literature are usually trained and evaluated on specific, individual datasets, i.e. with unique input data and tar...    »