MoatNet: Registration for Multi-Temporal Optical Remote Sensing Images Using Deep Convolutional Features

Image registration is an important technique that has been widely used in many areas. It is an indispensable premise for remote sensing image tasks like change detection and image fusion. In this paper, we propose a deep learning framework to generate descriptors for key points and then combine the descriptors constructed with FAST key points for accurate image registration. During the process of training, we adopt a novel loss function named Moat Loss (ML) to train our model, which is accordingly called MoatNet. Experiments show that our method is more robust than traditional algorithms like SIFT and is more accurate than the end-to-end deep learning methods in much more complex cases.