Object-Centric Masked Image Modelling for Self-Supervised Pre-Training in Remote Sensing object Detection

Abstract

The proliferation of remote sensing technologies has led to an increasing demand for effective object detection in satellite and aerial imagery, with applications ranging from environmental monitoring to urban planning. Traditional methods for analyzing such imagery often rely on manual inspection, which is both time-consuming and prone to human error. While recent advancements in automated object detection have improved efficiency, these systems frequently suffer from limitations in accurately identifying and classifying objects due to their reliance on simplistic masking techniques and insufficient context understanding.