SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis

Zhang, Tianwen; Zhang, Xiaoling; Li, Jianwei; Xu, Xiaowo; Wang, Baoyou; Zhan, Xu; Xu, Yanqin; Ke, Xiao; Zeng, Tianjiao; Su, Hao; Ahmad, Israr; Pan, Dece; Liu, Chang; Zhou, Yue; Shi, Jun; Wei, Shunjun

doi:10.3390/rs13183690

Cited by 301 publications

(178 citation statements)

References 110 publications

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“…The SAR Ship Detection Dataset (SSDD) is the first dataset for SAR imagery-based intelligent interpretation presented by Li et al In [48], vanilla SSDD with horizontal bounding box annotation is extended to pixel-level polygon segmentation SSDD (PSeg-SSDD), which supports the instance segmentation of SAR imagery in our work. Consistent with the data volume in SSDD, PSeg-SSDD contains 1160 SAR images in total with various polarizations, resolutions, and scenes.…”

Section: Sar Ship Detection Datasetmentioning

confidence: 54%

Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

et al. 2022

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Instance segmentation in remote sensing images is challenging due to the object-level discrimination and pixel-level segmentation for the objects. In remote sensing applications, instance segmentation adopts the instance-aware mask, rather than horizontal bounding box and oriented bounding box in object detection, or category-aware mask in semantic segmentation, to interpret the objects with the boundaries. Despite these distinct advantages, versatile instance segmentation methods are still to be discovered for remote sensing images. In this paper, an efficient instance segmentation paradigm (EISP) for interpreting the synthetic aperture radar (SAR) and optical images is proposed. EISP mainly consists of the Swin Transformer to construct the hierarchical features of SAR and optical images, the context information flow (CIF) for interweaving the semantic features from the bounding box branch to mask branch, and the confluent loss function for refining the predicted masks. Experimental conclusions can be drawn on the PSeg-SSDD (Polygon Segmentation—SAR Ship Detection Dataset) and NWPU VHR-10 instance segmentation dataset (optical dataset): (1) Swin-L, CIF, and confluent loss function in EISP acts on the whole instance segmentation utility; (2) EISP* exceeds vanilla mask R-CNN 4.2% AP value on PSeg-SSDD and 11.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The poorly segmented masks, false alarms, missing segmentations, and aliasing masks can be avoided to a great extent for EISP* in segmenting the SAR and optical images; (4) EISP* achieves the highest instance segmentation AP value compared to the state-of-the-art instance segmentation methods.

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Section: Sar Ship Detection Datasetmentioning

confidence: 54%

Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

et al. 2022

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“…First, two characteristics are used to obtain a lightweight network, i.e., (1) a LCB module is inserted into the backbone network of YOLOv5 and (2) network pruning is applied to obtain a more compact model. Then, four characteristics are used to guarantee the detection accuracy, i.e., (1) an HPCB module to effectively exclude pure background samples and suppress the false alarms; (2) a SDC method to generate superior priori anchor; (3) a CSA model to enhance the SAR ships semantic feature extraction ability; an (4) an H-SPP model to increase the context information of the receptive field. To evaluate the on-board SAR ship detection ability of Lite-YOLOv5, we also transplanted it to the embedded platform NVIDIA Jetson TX2.…”

Section: Discussionmentioning

confidence: 99%

“…First, to obtain a lightweight network, inspired by Han et al [26], a lightweight cross stage partial (L-CSP) module is inserted into the backbone network of the You Only Look Once version 5 (YOLOv5) algorithm [27] for reducing the amount of calculation; motivated by the network slimming algorithm proposed by Liu et al [28], we apply network pruning for a more compact model. Then, in order to compensate the detection accuracy, we (1) propose a histogram-based pure backgrounds classification (HPBC) module to effectively exclude pure background samples and suppress false alarms;…”

Section: Dl-based Lightweight Sarmentioning

confidence: 99%

“…Due to the all-day and all-weather ability of SAR, SAR remote sensing images have been widely applied in the field of ship detection. Currently, increasing number of scholars are paying attention to ship detection in SAR images due to its potential application in environmental monitoring, shipwreck rescue, oil leakage detection, marine shipping control [1][2][3][4], etc. Thus, it is of great significance to obtain the real-time and accurate ship detection results.…”

Section: Introductionmentioning

confidence: 99%

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Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Zhang

2022

Remote Sensing

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155

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Synthetic aperture radar (SAR) satellites can provide microwave remote sensing images without weather and light constraints, so they are widely applied in the maritime monitoring field. Current SAR ship detection methods based on deep learning (DL) are difficult to deploy on satellites, because these methods usually have complex models and huge calculations. To solve this problem, based on the You Only Look Once version 5 (YOLOv5) algorithm, we propose a lightweight on-board SAR ship detector called Lite-YOLOv5, which (1) reduces the model volume; (2) decreases the floating-point operations (FLOPs); and (3) realizes the on-board ship detection without sacrificing accuracy. First, in order to obtain a lightweight network, we design a lightweight cross stage partial (L-CSP) module to reduce the amount of calculation and we apply network pruning for a more compact detector. Then, in order to ensure the excellent detection performance, we integrate a histogram-based pure backgrounds classification (HPBC) module, a shape distance clustering (SDC) module, a channel and spatial attention (CSA) module, and a hybrid spatial pyramid pooling (H-SPP) module to improve detection performance. To evaluate the on-board SAR ship detection ability of Lite-YOLOv5, we also transplant it to the embedded platform NVIDIA Jetson TX2. Experimental results on the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) show that Lite-YOLOv5 can realize lightweight architecture with a 2.38 M model volume (14.18% of model size of YOLOv5), on-board ship detection with a low computation cost (26.59% of FLOPs of YOLOv5), and superior detection accuracy (1.51% F1 improvement compared with YOLOv5).

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“…In order to verify that the S-LPAN is not only better for the detection on small vessels, but also applicable to the detection of other sizes of vessels. The public dataset, SAR Ship Detection Dataset (SSDD), [58] is employed for experimental validation. The SSDD dataset images are mainly from RadarSat-2, TerraSAR-X, and Sentinel-1 sensors.…”

Section: S-lpanmentioning

confidence: 99%

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

Zhou

Zhang

et al. 2022

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

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Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized and the sea-land segmentation process to remove land false alarms is time-consuming. Regarding these problems, firstly, a single low-level path aggregation network (S-LPAN) is designed specifically for small target. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Secondly, adaptive dual-polarimetric feature fusion (ADPFF) is proposed to filter the multi-channel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Thirdly, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset (P-DSD) containing small vessel detection and sea-land segmentation labels is created with reference to the LS-SSDD v1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.

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SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis

Cited by 301 publications

References 110 publications

Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

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