Temporal Axial Attention For Lidar-Based 3D Object Detection in Autonomous Driving

Precise estimation of ground displacement at regional scales from optical satellite imagery is fundamental for the study of natural disasters, such as earthquakes, volcanoes, landslides, etc. Current methods make use of correlation techniques between two acquisitions in order to retrieve a fractional pixel shift. However, differences in local lighting conditions between two acquisitions can lead to differences in image reflectance, which in turn can bias the displacement estimate, especially in the sub-pixel domain. Data-driven methods may provide a way to overcome these errors. From the generation of a realistic simulated database based on Landsat-8 satellite image pairs with added simulated sub-pixel shifts, we developed a Convolutional Neural Network (CNN) able to retrieve sub-pixel displacements.