RFM-GAN: Robust Feature Matching With GAN-Based Neighborhood Representation for Agricultural Remote Sensing Image Registration
Yifu Liu, Yuyan Liu, Jiazhen Wang · IEEE Geoscience and Remote Sensing Letters · 2023
Remote sensing 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 remote sensing image registration is significantly compromised. This paper 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 multi-view 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.