Ship Detection in High-Resolution Optical Remote Sensing Images Aided by Saliency Information

Ren, Zhida; Tang, Yongqiang; He, Zewen; Tian, Lei; Yang, Yang; Zhang, Wensheng

doi:10.1109/tgrs.2022.3173610

Cited by 36 publications

(13 citation statements)

References 53 publications

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“…It can narrow down the detection range and enhance detection efficiency. Ren et al [70] added a saliency prediction branch to introduce saliency information with stronger foreground expression ability in SDORSIs. It improves the ship detection capability in complex environments.…”

Section: Saliency-constraint-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

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Section: Saliency-constraint-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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“…For remote sensing, LEO satellite observes and gathers earth resource data on low-earth orbit. Such data presents the features and changes of ground objects, and is often used for geographical applications [31][32] [33]. For example, farmland monitoring promotes agricultural harvesting production.…”

Section: Leo Satellites For Remote Sensing and On-orbit Computingmentioning

confidence: 99%

LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing

Li,

Wang,

Hwang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper proposes a new satellite-based framework for global-scale remote sensing that is integrated with on-orbit cloud computing and AI (artificial intelligence) services. These spaced-based services cover the entire earth surfaces using massive LEO satellite constellation. Global-scale sensing of earth resources must be supported by massive number of LEO satellites equipped with cloud/AI computing services in real time. New satellite computer architectural features are presented along with some satellite constellation deployment topologies.We design satellite-based computers to support on-orbit remote sensing and AI scene analysis. This demands real-time performance without transmitting the sensed data back to earth for delayed processing. Notable space data services include onorbit data sensing of large areas, machine learning from earth resources data, earth scene/event analysis, geomorphology observation, smart city management, disaster relief, global healthcare IoT, and environmental ecology protection, etc. We attempt to achieve high-efficiency earth resources utilization along with green energy, low cost, and robustness in real-life services.

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“…Song et al proposed an improved parallel ISSD ship detection algorithm based on the SSD target detection network [54]. By introducing a new feature extraction layer generated by a saliency dataset and the extraction layer of the original network for feature fusion [55], the ship recognition rate under the occlusion of thin-cloud and fog is effectively improved. This method effectively improves the ship recognition rate in the environment of thin-cloud and fog occlusion.…”

Section: B Ship Detection Strategy In Foggy Environmentmentioning

confidence: 99%

“…Unlike the images in computer vision (CV), such as Microsoft Common Objects in Context (COCO) [42] and Pascal Visual Object Classes (VOC) [43], targets in satellite remote sensing images are usually very small because remote sensing images are taken from a distance, usually hundreds or even thousands of kilometers from the ground. For example, in a remote sensing image of 1000×1000 pixels, a ship target only occupy 50×50 pixels [44][45]. In terms of algorithms, the mainstream target detection algorithms based on deep learning come from the field of CV.…”

Section: Introductionmentioning

confidence: 99%

FRS-Net: An Efficient Ship Detection Network for Thin-Cloud and Fog-Covered High-Resolution Optical Satellite Imagery

Zhang

Zheng

Cao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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According to statistics, more than 50% optical satellite images are covered by cloud or fog. Moreover, the cloud-cover rate is much higher in large bodies of water and nearby areas than inland areas due to the high amount of water evaporation and condensation. Therefore, ship detection from optical remote sensing images based on water surface analysis is more susceptible to cloud and fog interference, which affects the detection accuracy. In the time-sensitive application field of remote sensing images, due to the massive parameters in the large-scale network model, the detection speed is slow, and a lightweight detection model is commonly used. However, it is difficult for the lightweight detection model to achieve both high efficiency and accuracy for ship detection in a cloud-cover environment. To solve these problems, this manuscript proposes a lightweight algorithm called Fog Remote Sensing Ship Detection Network (FRS-Net) suitable for ship detection from remote sensing images in thin-cloud and fog covered environments. FRS-Net is developed based on the Deep Learning algorithm and can effectively improve ship detection accuracy under thin-cloud and fog cover. First, for the allocation strategy of anchor boxes, by using K-means clustering algorithm, FRS-Net simplifies the number of anchor boxes by utilizing the shape characteristics of the ship, which improves the time efficiency and the detection accuracy. Second, the FRS-Net network can meet the detection accuracy and has a fast inference speed. FRS-Net network is mainly composed of backbone extraction network, feature fusion network and prediction network. Experimental results on the Ship Detection in Optical Remote sensing images (SDIOR) dataset demonstrate the detection accuracy and computational efficiency of FRS-Net. The recognition mean Average Precision (mAP) achieved 43.20% for ship detection under thin-cloud and fog cover, with an efficiency of up to 424 FPS. FRS-Net has the potential to be applied in future scenarios such as embedded processing and on-board processing, where computing capabilities are strictly limited and the timeliness requirement is high.

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Ship Detection in High-Resolution Optical Remote Sensing Images Aided by Saliency Information

Cited by 36 publications

References 53 publications

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing

FRS-Net: An Efficient Ship Detection Network for Thin-Cloud and Fog-Covered High-Resolution Optical Satellite Imagery

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