A revievv of deep learning-based algorithms for object detection in satellite images

Abstract

Object detection in satellite images is a particularly interesting area in computer vision. This paper synthesizes and analyzes the challenges and characteristics of satellite images, as well as existing methods, with a special emphasis on the role of deep learning. The authors point out that object detection in satellite images is different from that in conventional images due to the high resolution, noise, and diversity of objects. To address these challenges, this paper introduces anchor-based and non-anchor-based methods in detail, and highlights the advantages and disadvantages of each method. In particular, the emergence of Transformer architectures in computer vision has opened up a new promising direction for object detection in satellite images. In addition, this paper also discusses practical applications of object detection in satellite images, including environmental monitoring, resource management, and disaster response. Finally, the paper suggests potential future research directions, such as developing more efficient models, handling small objects, and leveraging diverse data sources.