A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching

We propose a new method for remote sensing image matching. The proposed method uses an encoder subnetwork of an autoencoder pretrained on the GTCrossView data to construct image features. A discriminator network trained on the University of California Merced land-use/land-cover data set (LandUse) and the high-resolution satellite scene data set (SatScene) computes a match score between a pair of computed image features. We also propose a new network unit, called residual-dyad, and empirically demonstrate that networks that use residual-dyad units outperform those that do not. We compare our approach with both traditional and more recent learning-based schemes on the LandUse and SatScene data sets, and the proposed method achieves the state-of-the-art result in terms of mean average precision and average normalized modified retrieval rank (ANMRR) metrics. Specifically, our method achieves an overall improvement in performance of 11.26% and 22.41%, respectively, for LandUse and SatScene benchmark data sets.