Computer Vision and Graphics Lab1 Department of Computer Science Lahore School of Management Sciences Lahore, Pakistan
Visual Computing Lab2 Faculty of Science University of Ontario Institute of Technology 2000 Simcoe St. N., Oshawa ON L1G 0C5
We propose a new method for remote sensing image matching. The proposed method uses encoder subnetwork of an autoencoder pre-trained on GTCrossView data to construct image features. A discriminator network trained on University of California Merced Land Use/Land Cover dataset (LandUse) and High-resolution Satellite Scene dataset (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 LandUse and SatScene datasets, and the proposed method achieves state-of-the-art result in terms of mean average precision and ANMRR metrics. Specifically, our method achieves an overall improvement in performance of 11.26% and 22.41%, respectively, for LandUse and SatScene benchmark datasets.
For technical details please look at the following publications