SAR ship detection based on salience region extraction and multi-branch attention-Reference-Cited by-同舟云学术

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SAR ship detection based on salience region extraction and multi-branch attention

Published:2023-09 Issue: Volume:123 Page:103489
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Zha Cheng,Min Weidong,Han Qing,Xiong Xin^ORCID,Wang Qi,Xiang Hongyue

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference41 articles.

1. Training of Neural Network Target Detectors Mentored by SO-CFAR;Akhtar,2021

2. GO-CFAR Trained Neural Network Target Detectors;Akhtar,2019

3. Impact of covariance mismatched training samples on constant false alarm rate detectors;Besson;IEEE Trans. Signal Process.,2021

4. YOLOv4-5D: an effective and efficient object detector for autonomous driving;Cai;IEEE Trans. Instrum. Meas.,2021

5. Sea-land segmentation algorithm of SAR image based on Otsu method and statistical characteristic of sea area;Chen;J. Data Acquisit. Process.,2014

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1. SwinYOLOv7: Robust ship detection in complex synthetic aperture radar images;Applied Soft Computing;2024-07

2. YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI;International Journal of Applied Earth Observation and Geoinformation;2024-04

3. OASL: Orientation-aware adaptive sampling learning for arbitrary oriented object detection;International Journal of Applied Earth Observation and Geoinformation;2024-04

4. SSDFL: Spatial scattering decomposition feature learning for PolSAR image;International Journal of Applied Earth Observation and Geoinformation;2024-04

5. Subpixel Feature Pyramid Network for Multiscale Ship Detection in Synthetic Aperture Radar Remote Sensing Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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