Dense feature pyramid network for ship detection in SAR images

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance.

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Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection

Remote Sensing ◽

10.3390/rs13142771 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2771

Author(s):

Tianwen Zhang ◽

Xiaoling Zhang ◽

Xiao Ke

Keyword(s):

Traffic Control ◽

Fishery Management ◽

Sar Images ◽

Ship Detection ◽

Marine Fishery ◽

Multi Scale ◽

Marine Traffic ◽

Feature Pyramid ◽

Sar Imagery ◽

Small Ship

Ship detection from synthetic aperture radar (SAR) imagery is a fundamental and significant marine mission. It plays an important role in marine traffic control, marine fishery management, and marine rescue. Nevertheless, there are still some challenges hindering accuracy improvements of SAR ship detection, e.g., complex background interferences, multi-scale ship feature differences, and indistinctive small ship features. Therefore, to address these problems, a novel quad feature pyramid network (Quad-FPN) is proposed for SAR ship detection in this paper. Quad-FPN consists of four unique FPNs, i.e., a DEformable COnvolutional FPN (DE-CO-FPN), a Content-Aware Feature Reassembly FPN (CA-FR-FPN), a Path Aggregation Space Attention FPN (PA-SA-FPN), and a Balance Scale Global Attention FPN (BS-GA-FPN). To confirm the effectiveness of each FPN, extensive ablation studies are conducted. We conduct experiments on five open SAR ship detection datasets, i.e., SAR ship detection dataset (SSDD), Gaofen-SSDD, Sentinel-SSDD, SAR-Ship-Dataset, and high-resolution SAR images dataset (HRSID). Qualitative and quantitative experimental results jointly reveal Quad-FPN’s optimal SAR ship detection performance compared with the other 12 competitive state-of-the-art convolutional neural network (CNN)-based SAR ship detectors. To confirm the excellent migration application capability of Quad-FPN, the actual ship detection in another two large-scene Sentinel-1 SAR images is conducted. Their satisfactory detection results indicate the practical application value of Quad-FPN in marine surveillance.

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Ship Detection in SAR Images via Enhanced Nonnegative Sparse Locality-Representation of Fisher Vectors

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3042506 ◽

2021 ◽

pp. 1-15

Author(s):

Xueqian Wang ◽

Gang Li ◽

Antonio Plaza ◽

You He

Keyword(s):

Sar Images ◽

Ship Detection

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Ship Detection in Large Scale Sar Images Based on Bias Classification

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324527 ◽

2020 ◽

Author(s):

Xiaoya Wang ◽

Zongyong Cui ◽

Zongjie Cao ◽

Yu Tian

Keyword(s):

Large Scale ◽

Sar Images ◽

Ship Detection

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TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

Remote Sensing ◽

10.3390/rs13132558 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2558

Author(s):

Lei Yu ◽

Haoyu Wu ◽

Zhi Zhong ◽

Liying Zheng ◽

Qiuyue Deng ◽

...

Keyword(s):

Target Detection ◽

Detection Methods ◽

Observation System ◽

Single Shot ◽

Small Target ◽

Feature Mapping ◽

Sar Images ◽

Two Stage ◽

Ship Detection ◽

Deep Feature

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

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