Abstract:
Remote sensing (RS) images often encounter various challenges arising from differences in shooting time, location, equipment, sensors, and other factors. These disparitie...Show MoreMetadata
Abstract:
Remote sensing (RS) images often encounter various challenges arising from differences in shooting time, location, equipment, sensors, and other factors. These disparities lead to image distortion and insufficient overlap between pairs of images captured at the same location. Consequently, the accuracy of agricultural RS image registration is significantly compromised. This letter proposes a robust feature-matching technique called GAN-based neighborhood representation (RFM-GAN) for the intricate registration of satellite images and unmanned aerial vehicle (UAV) images. The RFM-GAN method leverages a neighborhood representation approach based on a generative adversarial network (GAN) with two discriminators. This representation enhances the distinction between true matches (inliers) and false matches (outliers). Additionally, a dissimilarity measure network employing a self-supervised training approach, eliminating the need for manual labeling, is designed to handle the multiview transformation of satellite and UAV images. The experimental results confirm that RFM-GAN outperforms seven other state-of-the-art methods in terms of satellite image and UAV image processing.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)