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Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

Kyamelia Roy, Sheli Sinha Chaudhuri, Sayan Pramanik, Soumen Banerjee
2023 Computer systems science and engineering  
The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats  ...  The experimental results reveal that the model is accurate and can be implemented for automatic ship detection in water bodies considering remote sensing satellite images as input to the computer vision  ...  In [25] , a fully convolutional network (FCN) with task partitioning for inshore ship detection in optical remote sensing images is reported.  ... 
doi:10.32604/csse.2023.024997 fatcat:prqcgnyrhjbzhcyqd3bgpyza7u

S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

Ruiqian Zhang, Jian Yao, Kao Zhang, Chen Feng, Jiadong Zhang
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted  ...  Reliable ship detection plays an important role in both military and civil fields.  ...  In summary, all these methods take significant efforts on ship detection in optical images.  ... 
doi:10.5194/isprs-archives-xli-b7-423-2016 fatcat:f5bg5ocevffjnkdl4i5c75xwgy

S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

Ruiqian Zhang, Jian Yao, Kao Zhang, Chen Feng, Jiadong Zhang
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted  ...  Reliable ship detection plays an important role in both military and civil fields.  ...  In summary, all these methods take significant efforts on ship detection in optical images.  ... 
doi:10.5194/isprsarchives-xli-b7-423-2016 fatcat:ffdlmyvhnnh4rouknlurbwbz5y

CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection

Runfan Xia, Jie Chen, Zhixiang Huang, Huiyao Wan, Bocai Wu, Long Sun, Baidong Yao, Haibing Xiang, Mengdao Xing
2022 Remote Sensing  
In response, for SAR target detection tasks, this paper combines the global contextual information perception of transformers and the local feature representation capabilities of convolutional neural networks  ...  In addition, based on the HISEA-1 commercial SAR satellite, which has been launched into orbit and in whose development our research group participated, we released a larger-scale SAR multiclass target  ...  Acknowledgments: We would like to thank the anonymous reviewers for their constructive and valuable suggestions on the earlier drafts of this manuscript.  ... 
doi:10.3390/rs14061488 fatcat:vychv7ewcnh6rhjnqfxpqgurbe

Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multi-Scale Convolutional Network [article]

Lingyi Liu, Yunpeng Bai, Ying Li
2020 arXiv   pre-print
In this letter, we propose a locality-aware rotated ship detection (LARSD) framework based on a multi-scale convolutional neural network (CNN) to tackle these issues.  ...  Furthermore, to enlarge the datasets of ship detection, we build a new high-resolution ship detection (HRSD) dataset, where 2499 images and 9269 instances were collected from Google Earth with different  ...  [14] proposed a rotated ship detector based on a fully convolutional network.  ... 
arXiv:2007.12326v1 fatcat:sttqgafnqfaghlktd2xbntn464

Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks

Antonio-Javier Gallego, Antonio Pertusa, Pablo Gil
2018 Remote Sensing  
The proposed architecture is based on Convolutional Neural Networks (CNN), and it combines neural codes extracted from a CNN with a k-Nearest Neighbor method so as to improve performance.  ...  The experimentation shows a success rate of over 99% for our approach, in contrast with the 79% obtained with traditional methods in classification of ship images, also outperforming other methods based  ...  The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  ... 
doi:10.3390/rs10040511 fatcat:o6o7ldkgjbfspjjf525bmfkgou

A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

Liqiong Chen, Wenxuan Shi, Cien Fan, Lian Zou, Dexiang Deng
2020 Remote Sensing  
In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems.  ...  Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control.  ...  Some targets are missed when inshore ships are adjacent to the land, and shoreline detection or specially-designed convolutional neural networks may be one solution.  ... 
doi:10.3390/rs12193115 doaj:0f194f60270f45239f97e7f9f831b0ed fatcat:pkgrfy7qfzat7hwzibzjj5znki

An Improved Lightweight RetinaNet for Ship Detection in SAR Images

Tian Miao, Hongcheng Zeng, Wei Yang, Boce Chu, Fei Zou, Weijia Ren, Jie Chen
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
As an important application of SAR, fast and accurate ship detection has always been a research hotspot. In this paper, an improved lightweight RetinaNet for ship detection in SAR images is proposed.  ...  Moreover, the experimental results also show the proposed model's robustness and the ability to detect ship targets in small datasets.  ...  CONCLUSION An improved ship detection network for SAR images based on RetinaNet is proposed.  ... 
doi:10.1109/jstars.2022.3180159 fatcat:sv2xrq674ngpdekxonw6qcf2hm

Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion

Yulian Zhang, Lihong Guo, Zengfa Wang, Yang Yu, Xinwei Liu, Fang Xu
2020 Remote Sensing  
To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts.  ...  In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod.  ...  An [27] used a deep convolutional neural network to detect ships in Gaofen-3 SAR images.  ... 
doi:10.3390/rs12203316 fatcat:q66erqvarzgwpje7p4tjhspp6m

Attention Receptive Pyramid Network for Ship Detection in SAR Images

YAN ZHAO, Lingjun Zhao, Boli Xiong, Gangyao Kuang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
, Dense Attention Pyramid Network, Depth-wise Separable Convolutional Neural Network, High-Resolution Ship Detection Network, and Squeeze and Excitation Rank Faster-RCNN.  ...  To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset and two large-scene SAR images.  ...  [40] proposed a Depth-wise Separable Convolutional Neural Network (DS-CNN) for ship detection in SAR images.  ... 
doi:10.1109/jstars.2020.2997081 fatcat:pplmyfehgbfinbvi4dzdh4zf6e

HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

Shunjun Wei, Xiangfeng Zeng, Qizhe Qu, Mou Wang, Hao Su, Jun Shi
2020 IEEE Access  
Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective shortcomings, such as the relatively  ...  INDEX TERMS High-resolution SAR images dataset, ship detection, instance segmentation, deep learning, convolutional neural network.  ...  have referenced the idea of YOLO series algorithm and proposed the grid convolutional neural network (G-CNN) for real-time SAR ship detection [36] . Wang et al.  ... 
doi:10.1109/access.2020.3005861 fatcat:r2tslmyep5c3fgjiwmbysqrprq

End-to-End Ship Detection in SAR Images for Complex Scenes Based on Deep CNNs

Yao Chen, Tao Duan, Changyuan Wang, Yuanyuan Zhang, Mo Huang, Jaime Lloret
2021 Journal of Sensors  
To balance the accuracy and speed, an end-to-end ship detection method for complex inshore and offshore scenes based on deep convolutional neural networks (CNNs) is proposed in this paper.  ...  First, the SAR images are divided into different grids, and the anchor boxes are predefined based on the responsible grids for dense ship prediction.  ...  We thank European Space Agency (ESA) for providing Sentinel-1 images and MarineTraffic website for providing Automatic Identification System (AIS) messages.  ... 
doi:10.1155/2021/8893182 fatcat:wvuctuqdwnfy3gphfwyuevldi4

SRSDD-v1.0: A High-Resolution SAR Rotation Ship Detection Dataset

Songlin Lei, Dongdong Lu, Xiaolan Qiu, Chibiao Ding
2021 Remote Sensing  
Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets.  ...  In addition, most of the images in the dataset contain nearshore areas with complex background interference.  ...  Acknowledgments: We thank the National Satellite Ocean Application Service for providing us with data. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13245104 fatcat:jn5dhjmuxfdqviik2vmsjnwefa

Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet

Wei, Su, Ming, Wang, Yan, Kumar, Shi, Zhang
2020 Remote Sensing  
In this paper, a novel ship detection method based on a high-resolution ship detection network (HR-SDNet) for high-resolution SAR imagery is proposed.  ...  Finally, the experimental results on the SAR ship detection dataset (SSDD) and TerraSAR-X high-resolution images reveal that (1) our approach based on the HRFPN has superior detection performance for both  ...  [32] applied spectral residual based on land-sea segmentation to realize automatic selecting the candidate ship location and convolutional neural networks to ship discrimination. Kang et al.  ... 
doi:10.3390/rs12010167 fatcat:fjmzavszgncldbzsy6wvownxji

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 1772-1783 Small Sample Set Inshore Ship Detection From VHR Optical Remote Sensing Images Based on Structured Sparse Representation.  ...  ., +, JSTARS 2020 1842-1855 Small Sample Set Inshore Ship Detection From VHR Optical Remote Sensing Images Based on Structured Sparse Representation.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
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