Sensors, Vol. 22, Pages 1231: Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network

Sensors, Vol. 22, Pages 1231: Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network Sensors doi: 10.3390/s22031231 Authors: Mykhail Uss Benoit Vozel Vladimir Lukin Kacem Chehdi Finding putative correspondences between a pair of images is an important prerequisite for image registration. In complex cases such as multimodal registration, a true match could be less plausible than a false match within a search zone. Under these conditions, it is important to detect all plausible matches. This could be achieved by an exhaustive search using a handcrafted similarity measure (SM, e.g., mutual information). It is promising to replace handcrafted SMs with deep learning ones that offer better performance. However, the latter are not designed for an exhaustive search of all matches but for finding the most plausible one. In this paper, we propose a deep-learning-based solution for exhaustive multiple match search between two images within a predefined search area. We design a computationally efficient convolutional neural network (CNN) that takes as input a template fragment from one image, a search fragment from another image and produces an SM map covering the entire search area in spatial dimensions. This SM map finds multiple plausible matches, locates each match with subpixel accuracy and provides a covariance matrix of localization errors for each match. The proposed CNN is trained with a specially designed l...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research