Monitoring Maritime Ship Characteristics Using Satellite Remote Sensing Data from Different Sensors

With the rapid development of intelligent information technologies, remote sensing object detection has played an important role in different field applications. Particularly in recent years, it has attracted widespread attention in assisting with food safety supervision, which still faces troubling issues between oversized parameters and low performance that are challenging to solve. Hence, this article proposes a novel remote sensing detection framework for multi-scale objects with a rotating status and mutual occlusion, defined as EYMR-Net. This proposed approach is established on the YOLO-v7 architecture with a Swin Transformer backbone, which offers multi-scale receptive fields to mine massive features. Then, an enhanced attention module is added to exploit the spatial and dimensional interrelationships among different local characteristics. Subsequently, the effective rotating frame regression mechanism via circular smoothing labels is introduced to the EYMR-Net structure, addressing the problem of horizontal YOLO (You Only Look Once) frames ignoring direction changes. Extensive experiments on DOTA datasets demonstrated the outstanding performance of EYMR-Net, which achieved an impressive mAP0.5 of up to 74.3%. Further ablation experiments verified that our proposed approach obtains a balance between performance and efficiency, which is beneficial for practical remote sensing applications in transportation monitoring and supply chain management.

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Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model

Aldoğan,

Aksu,

Demirel

2024

Applied Sciences

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Ship detection holds great value regarding port management, logistics operations, ship security, and other crucial issues concerning surveillance and safety. Recently, ship detection from optical satellite imagery has gained popularity among the research community because optical images are easily accessible with little or no cost. However, these images’ quality and quantity of feature details are bound to their spatial resolution, which often comes in medium-low spatial resolution. Accurately detecting ships requires images with richer texture and resolution. Super-resolution is used to recover features in medium-low resolution images, which can help leverage accuracy in ship detection. In this regard, this paper quantitatively and visually investigates the effectiveness of super-resolution in enabling more accurate ship detection in medium spatial resolution images by comparing Sentinel-2A images and enhanced Sentinel-2A images. A collection of Sentinel-2A images was enhanced four times with a Real-ESRGAN model that trained PlanetScope images with high spatial resolution. Separate ship detections with YOLOv10 were implemented for Sentinel-2A images and enhanced Sentinel-2A images. The visual and metric results of both detections were compared to demonstrate the contributory effect of enhancement on the ships’ detection accuracy. Ship detection on enhanced Sentinel-2A images has a mAP50 and mAP50-95 value of 87.5% and 68.5%. These results outperformed the training process on Sentinel-2A images with a mAP value increase of 2.6% for both mAP50 and mAP50-95, demonstrating the positive contribution of super-resolution.

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Monitoring Maritime Ship Characteristics Using Satellite Remote Sensing Data from Different Sensors

Cited by 3 publications

References 98 publications

Unveiling the Hydrological NDWI: Random Forest Analysis of Landsat Images - Siruvani Dam, India

Unveiling the Hydrological NDWI: Random Forest Analysis of Landsat Images - Siruvani Dam, India

Enhancing a You Only Look Once-Plated Detector via Auxiliary Textual Coding for Multi-Scale Rotating Remote Sensing Objects in Transportation Monitoring Applications

Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model

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