Ship Classification and Detection Based on CNN Using GF-3 SAR Images

Ma, Mengyuan; Chen, Jie; Liu, Wei; Yang, Wei

doi:10.3390/rs10122043

Cited by 78 publications

(41 citation statements)

References 35 publications

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“…To solve these problems, in this work, we achieve the classification of 19 classes of ships with large differences in scale and characteristics based on the patches from the output of our ship detector. The work most similar to ours is that of Ma et al [32]. However, they used a horizontal bounding box, which cannot handle the inshore vessels and only classified sparse ships in the sea with a single background into eight categories.…”

Section: Introductionmentioning

confidence: 99%

Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

et al. 2019

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Ship category classification in high-resolution aerial images has attracted great interest in applications such as maritime security, naval construction, and port management. However, the applications of previous methods were mainly limited by the following issues: (i) The existing ship category classification methods were mainly to classify on accurately-cropped image patches. This is unsatisfactory for the results of the existing methods in practical applications, because the location of the ship in the patch obtained by the object detection varies greatly. (ii) The factors such as target scale variations and class imbalance have a great influence on the performance of ship category classification. Aiming at the issues above, we propose a novel ship detection and category classification framework. The category classification is based on accurate location. The detection network can generate more precise rotated bounding boxes in large-scale aerial images by introducing a novel Sequence Local Context (SLC) module. Besides, three different ship category classification networks are proposed to eliminate the effect of scale variations, and the Spatial Transform Crop (STC) operation is used to get aligned image patches. Whatever the problem of insufficient samples or class imbalance have, the Proposals Simulation Generator (PSG) is considered to handle this properly. Most remarkably, the state-of-the-art performance of our framework is demonstrated by experiments based on the 19-class ship dataset HRSC2016 and our multiclass warship dataset.

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Section: Introductionmentioning

confidence: 99%

Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

et al. 2019

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“…With the rise of AI, deep learning [71] is providing much power for SAR ship detection. Based on our survey [8,61,62,70], deep learning has almost dominated the SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc., so increasingly, scholars have made use of deep learning-based ship detection act in an important research direction. In the early stage, deep learning was applied in various parts of SAR ship detection, e.g., land masking [28], region of interest (ROI) extraction, and ship discrimination [28,72] (i.e., ship or background binary classification of a single chip).…”

Section: Introductionmentioning

confidence: 99%

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Zhang

et al. 2020

Remote Sensing

163

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Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology. LS-SSDD-v1.0 is available at https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN.

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“…Gaofen-3 (GF-3) satellite is China's first civil C-band high-resolution quad-pol SAR satellite specifically missioned for ocean remote sensing. The nominal highest resolution of GF-3 data is up to 1 m. GF-3 data have been widely used on applications such as ship recognition [6][7][8][9][10], aircraft detection [11], and image translation between optical and SAR images [12].…”

Section: Introductionmentioning

confidence: 99%

FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

Hou

Song

et al. 2020

Sci. China Inf. Sci.

136

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Gaofen-3 (GF-3) is China's first civil C-band fully polarimetric spaceborne synthetic aperture radar (SAR) primarily missioned for ocean remote sensing and marine monitoring. This paper proposes an automatic sea segmentation, ship detection, and SAR-AIS matchup procedure and an extensible marine target taxonomy of 15 primary ship categories, 98 sub-categories, and many non-ship targets. The FUSAR-Ship high-resolution GF-3 SAR dataset is constructed by running the procedure on a total of 126 GF-3 scenes covering a large variety of sea, land, coast, river and island scenarios. It includes more than 5000 ship chips with AIS messages as well as samples of strong scatterer, bridge, coastal land, islands, sea and land clutter. FUSAR-Ship is intended as an open benchmark dataset for ship and marine target detection and recognition. A preliminary 8-type ship classification experiment based on convolutional neural networks demonstrated that an average of 79% test accuracy can be achieved.

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Ship Classification and Detection Based on CNN Using GF-3 SAR Images

Cited by 78 publications

References 35 publications

Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

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