Uncertainty of Patch Matching for Isotropic and Anisotropic Textures of Multimodal Remote Sensing Images

Image registration aims at aligning two or more images in a common coordinate system. A common approach is to find a geometric transformation that explains the parameters of putative correspondences between images. Each correspondence measures the spatial translation between local patches of the registered images. The uncertainty of this translation is important for registration task allowing to weight each correspondence with respect to others. Measuring uncertainty is challenging especially for multimodal registration case and for anisotropic textures. In this paper, we develop further a learning-based patch matching approach that predicts the covariation matrix of the translation error for each correspondence. We identify the bias related to the orientation of anisotropic textures and propose additional data augmentation and a modified loss function to eliminate it. Using multimodal remote sensing images, we confirm that the proposed approach predicts the covariation matrix of translation errors for both isotropic and anisotropic textures with different orientations.