Ship detection in high spatial resolution remote sensing image based on improved sea-land segmentation

Li, Na; Zhang, Qiaochu; Zhao, Huijie; Dong, Chao; Meng, Lingjie

doi:10.1117/12.2246390

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…This involves approaches based on the threshold, salient, shape and texture, statistics, transfer domain, computer vision, and deep learning methods [46]. Many studies detected inshore vessels using very high resolution (<1 m spatial resolution) satellite data for precise extraction of the objects [39,[47][48][49][50]. Currently, the majority (over 98%) of registered boats at the three harbors are motor fishing vessels (MFV) and fiber-reinforced plastic (FRP) [29].…”

Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

et al. 2021

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The COVID-19 related lockdowns have brought the planet to a standstill. It has severely shrunk the global economy in the year 2020, including India. The blue economy and especially the small-scale fisheries sector in India have dwindled due to disruptions in the fish catch, market, and supply chain. This research presents the applicability of satellite data to monitor the impact of COVID-19 related lockdown on the Indian fisheries sector. Three harbors namely Mangrol, Veraval, and Vankbara situated on the north-western coast of India were selected in this study based on characteristics like harbor’s age, administrative control, and availability of cloud-free satellite images. To analyze the impact of COVID in the fisheries sector, we utilized high-resolution PlanetScope data for monitoring and comparison of “area under fishing boats” during the pre-lockdown, lockdown, and post-lockdown phases. A support vector machine (SVM) classification algorithm was used to identify the area under the boats. The classification results were complemented with socio-economic data and ground-level information for understanding the impact of the pandemic on the three sites. During the peak of the lockdown, it was found that the “area under fishing boats” near the docks and those parked on the land area increased by 483%, 189%, and 826% at Mangrol, Veraval, and Vanakbara harbor, respectively. After phase-I of lockdown, the number of parked vessels decreased, yet those already moved out to the land area were not returned until the south-west monsoon was over. A quarter of the annual production is estimated to be lost at the three harbors due to lockdown. Our last observation (September 2020) result shows that regular fishing activity has already been re-established in all three locations. PlanetScope data with daily revisit time has a higher potential to be used in the future and can help policymakers in making informed decisions vis-à-vis the fishing industry during an emergency situation like COVID-19.

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Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

et al. 2021

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“…Li et al [87] presented a novel method based on a combination of mean shift and modified Otsu's method for sea-land segmentation. After that, ship detection from a highresolution remote sensing image.…”

Section: Modava and Akbarizadehmentioning

confidence: 99%

Current Trends and Future Directions in Sea-Land Segmentation for Remote Sensing Images

Elkhateeb,

Mekky,

Atwan

et al. 2019

Mansoura Journal for Computer and Information Sciences

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Nowadays, sea-land segmentation for remote sensing images has a valuable role in water resources monitoring, maritime safety, and coastal zones management. However, it has faced many challenges such as the complicated distribution of land area, noise, poor contrast between sea and land regions, different weather conditions, the development of sensors, and high-resolution images provide more information. Consequently, there are considerable efforts have been made to develop various methods to overcome these challenges. Therefore, this paper introduces the description of the main steps of the sea-land segmentation procedure and the main characteristics of each step. Also, the paper focuses on the taxonomy of the current sea-land segmentation methods. These methods are broadly categorized into six main groups namely thresholding-based methods, region-based methods, energy minimization-based methods, machine learning-based methods, watershed transformation-based methods, and hybrid methods. Finally, this paper also shows and discusses the common challenges which are facing the sea-land segmentation. Besides, the paper introduces promising future research directions in the sea-land segmentation field.

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“…With the improvement of the resolution of optical remote sensing images, the limited spatial resolution of the existing geo-location information database can no longer meet the demand for fine sea–land segmentation. Segmentation based on grayscale and texture [30,31,32] is not only sensitive to the parameters selected, but also prone to misclassification. Also, segmentation using grayscale and texture information needs to be performed on the entire image.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Negative Sample Training for Ship Detection Based on Deep Learning

Gao

Sun

et al. 2019

Sensors

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While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea–land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.

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Ship detection in high spatial resolution remote sensing image based on improved sea-land segmentation

Cited by 4 publications

References 6 publications

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

Current Trends and Future Directions in Sea-Land Segmentation for Remote Sensing Images

Incorporating Negative Sample Training for Ship Detection Based on Deep Learning

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