Enhanced Vehicle Detection and Segmentation Using the SAMRS Model: Applications in High-Resolution Satellite Imagery

Deep learning technologies have revolutionized image processing and analysis, introducing groundbreaking innovations that significantly improve the accuracy and efficiency of object segmentation, especially in satellite imagery.The increasing availability of high-resolution satellite images has created a demand for advanced models capable of handling the complexities of object detection in diverse environments.This study investigates the potential of the Segment Anything Model for Remote Sensing (SAMRS), a deep learning framework specifically designed for remote sensing applications, to accurately identify and segment a wide range of objects within satellite imagery.The model was trained using prominent datasets such as Dataset for Object Detection in Aerial Images (DOTA), Dataset for Object Detection in Optical Remote Sensing Images (DIOR), Fine-grained Object Detection in Aerial Images for Remote Sensing Version 2.0 (FAIR1M-2.0),and Instance Segmentation in Aerial Images Dataset (iSAID), enabling it to learn diverse object features and complexities.The evaluation of SAMRS was conducted on Northwestern Polytechnical University Very High Resolution 10-Class Dataset (NWPU VHR-10) and Beijing-3B datasets, where it demonstrated impressive results.In vehicle detection tasks, SAMRS achieved an Intersection over Union (IoU) of 0.9175, an F1-score of 0.9570, and an accuracy of 0.9385.These metrics highlight SAMRS's capability to automate object detection in complex satellite images, overcoming challenges posed by intricate backgrounds and diverse object sizes.Furthermore, SAMRS is optimized to analyze both large and small-scale objects, ensuring robust performance across varying conditions.The findings emphasize the model's utility not only for current remote sensing applications but also for future extensions involving drone imagery and domestic satellite datasets.By automating object detection and segmentation, SAMRS has the potential to transform practical fields such as urban planning, disaster management, traffic monitoring, and environmental analysis, making it a vital tool in advancing satellite imagery analysis.