Investigation of Deep Learning Methodologies in Satellite Image based Ship Detection-Reference-Cited by-同舟云学术

Investigation of Deep Learning Methodologies in Satellite Image based Ship Detection

Published:2022-04-07 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)
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Author:

Joseph S Iwin Thanakumar¹^ORCID,Karunakaran V²,Sujatha T³,Edwin Rai S Benson⁴,Velliangiri S⁵

Affiliation:

1. Koneru Lakshmaiah Education Foundation,Department of Computer Science and Engineering,Vijayawada,India

2. Jain University,Department of Computer Science and Engineering(AI&ML) and (Cybersecurity),Bengaluru,India

3. Sri Krishna College of Engineering and Technology,Department of AI & DS,Coimbatore,India

4. Higher Colleges of Technology,Department of Computer Information Sciences,Fujairah,United Arab Emirates

5. B V Raju Institute of Technology,Department of Computer Science and Engineering,Hyderabad,Telengana,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9760735/9760712/09760824.pdf?arnumber=9760824

Reference21 articles.

1. Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

2. Ship detection in satellite imagery by multiple classifier network;siyal;International Journal of Computer Science and Network Security (IJCSNS),2019

3. Ship classification in TerraSAR-X images with convolutional neural networks;bentes;IEEE Journal of Oceanic Engineering,2017

4. Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network

5. A survey of remote sensing image classification based on CNNs

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images;Remote Sensing;2023-09-04

2. FRS-Net: An Efficient Ship Detection Network for Thin-Cloud and Fog-Covered High-Resolution Optical Satellite Imagery;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2022