Generative Adversarial Networks in Object Detection and Segmentation in Remote Sensing Images

Abstract

GANs are revolutionizing computer vision, especially in remote sensing through satellite and aerial imagery. These images pose unique challenges: they're complex and contain objects of various sizes, making segmentation difficult. This paper explores how GANs overcome these challenges by generating realistic synthetic data, particularly when labeled data is scarce. We examine specialized variants like cGANs and SegGANs, which excel in land use analysis, urban structure detection, and environmental monitoring. Our approach combines GANs with traditional machine learning to improve object detection accuracy beyond current standards. While acknowledging cost and interpretability challenges, we highlight GANs' potential in multi-spectral and hyperspectral imaging applications.