Balance learning for ship detection from synthetic aperture radar remote sensing imagery

2021 ◽  
Vol 182 ◽  
pp. 190-207
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang ◽  
Chang Liu ◽  
Jun Shi ◽  
Shunjun Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery ◽  
Ship Detection ◽  
Aperture Radar

Multi-scale synthetic aperture radar remote sensing for archaeological prospection in Han Hangu Pass, Xin'an China

2016 ◽  
Vol 8 (1) ◽  
pp. 38-47 ◽  
Author(s):  
Fulong Chen ◽  
Aihui Jiang ◽  
Panpan Tang ◽  
Ruixia Yang ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Archaeological Prospection ◽  
Multi Scale ◽  
Aperture Radar

scholarly journals Wetlands and Malaria in the Amazon: Guidelines for the Use of Synthetic Aperture Radar Remote-Sensing

2018 ◽  
Vol 15 (3) ◽  
pp. 468 ◽  
Author(s):  
Thibault Catry ◽  
Zhichao Li ◽  
Emmanuel Roux ◽  
Vincent Herbreteau ◽  
Helen Gurgel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Aperture Radar

Application of synthetic aperture radar remote sensing in Antarctica

2014 ◽  
Author(s):  
Chunxia Zhou ◽  
Fanghui Deng ◽  
Lei Wan ◽  
Zemin Wang ◽  
Dongchen E ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Aperture Radar

scholarly journals Combinational Fusion and Global Attention of the Single-Shot Method for Synthetic Aperture Radar Ship Detection

2021 ◽  
Vol 13 (23) ◽  
pp. 4781
Author(s):  
Libo Xu ◽  
Chaoyi Pang ◽  
Yan Guo ◽  
Zhenyu Shu
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Loss Function ◽  
Global Perspective ◽  
Synthetic Aperture ◽  
Single Shot ◽  
Sar Images ◽  
Ship Detection ◽  
Function Block ◽  
Aperture Radar

Synthetic Aperture Radar (SAR), an active remote sensing imaging radar technology, has certain surface penetration ability and can work all day and in all weather conditions. It is widely applied in ship detection to quickly collect ship information on the ocean surface from SAR images. However, the ship SAR images are often blurred, have large noise interference, and contain more small targets, which pose challenges to popular one-stage detectors, such as the single-shot multi-box detector (SSD). We designed a novel network structure, a combinational fusion SSD (CF-SSD), based on the framework of the original SSD, to solve these problems. It mainly includes three blocks, namely a combinational fusion (CF) block, a global attention module (GAM), and a mixed loss function block, to significantly improve the detection accuracy of SAR images and remote sensing images and maintain a fast inference speed. The CF block equips every feature map with the ability to detect objects of all sizes at different levels and forms a consistent and powerful detection structure to learn more useful information for SAR features. The GAM block produces attention weights and considers the channel attention information of various scale feature information or cross-layer maps so that it can obtain better feature representations from the global perspective. The mixed loss function block can better learn the positions of the truth anchor boxes by considering corner and center coordinates simultaneously. CF-SSD can effectively extract and fuse the features, avoid the loss of small or blurred object information, and precisely locate the object position from SAR images. We conducted experiments on the SAR ship dataset SSDD, and achieved a 90.3% mAP and fast inference speed close to that of the original SSD. We also tested our model on the remote sensing dataset NWPU VHR-10 and the common dataset VOC2007. The experimental results indicate that our proposed model simultaneously achieves excellent detection performance and high efficiency.

scholarly journals Ship Detection Based on Information Theory and Segmentation from Synthetic Aperture Radar (SAR) Images

2020 ◽  
Vol 8 (6) ◽  
pp. 2513-2517
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Satellite Images ◽  
Synthetic Aperture ◽  
Morphological Operations ◽  
Sar Images ◽  
Ship Detection ◽  
Radar Images ◽  
Vessel Detection ◽  
Aperture Radar

Ship detection is a procedure which asserts in fields such as ocean and sea management, vessel detection, marine superintendence, and rein, and also can be applied to exclude extralegal actions. Remote sensing can be utilized as a potential tool for zonular and universal monitoring to attain the forenamed goals. Among the radar images, the precious datum from Synthetic Aperture Radar (SAR) is playing a serious duty in remote sensing. Howsoever, vessel detecting in heterogeneous and strong clutter is still a question in this regard. The letter points to a ship detection scheme for SAR images exploiting a segmentation-based morphological operation using entropy. In the presented scheme, the morphological operations are adopted to intercept the background and foreground in the satellite images. The method was implemented and tested on the homogenous, heterogeneous and strong clutter SAR images and the results are promising and showing that the proposed method can improve the vessel detection from homogenous and heterogeneous and strong clutter satellite images

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