A Comparative Study of Deep Learning-Based Models for Object Detection in Remote Sensing Imagery

Abstract. Object detection contributes significantly to advancing image interpretation and understanding. The advent of deep learning-based methods has significantly advanced this field. However, the distinctive characteristics of remote sensing images, including large directional variations, scale differences, and complex and cluttered backgrounds, pose considerable challenges for accurate target detection. In this work, we compare the detection accuracy and processing speed of several state-of-the-art models by detecting palm trees in optical satellite imagery. This work aims to explore how these models, adopted in many remote sensing applications, perform when applied to detect objects in overhead satellite images. Several models are selected from the single-stage and two-stage object detection families of techniques. Additionally, we use the timing results of the sliding window object detector to establish a baseline to compare different approaches. Our experiments demonstrate that two-stage detectors perform better in remote sensing contexts when detecting small, crowded objects, outperforming their single-stage counterparts. Future work includes extending this analysis to additional models, such as the multi-stage object detection family.