Deep Strategy of Object Detection in Remote Sensing Images

Abstract

Recently, the demand for satellite image analysis increased with the recent advancement in various research areas (e.g., improvements in remote sensing image resolution, object recognition ideas, and deep learning techniques). Articles report new trends in object detection using remote sensing images, such as multi-temporal scene classifications, semantic segmentation, multiresolution, and large-scale optical image analysis. However, this study observed a black box of synchronization among the advancements in various technologies (i.e., new ideas in satellite image analysis) and advancements in deep learning techniques. Without investigating this black box, the optimization of object detection will be out of track. So, to keep the deep learning innovation on track, the primary goal of the current work is to explore the aforementioned black box to advance object detection in remote sensing images. In contrast, focusing on a specific technique, this study explores the black box while reviewing 150 articles from 2019 to 2023. First, the study highlights research methodologies and novel features of literature to achieve object detection in remote sensing images. Second, it evaluates effective deep learning models and assesses various featured studies to draw a clear picture of limitations and recent object detection trends to provide in-depth recommendations and future directions.