Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing-Reference-Cited by-同舟云学术

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Song Juyoung¹^ORCID,Kim Duk-jin¹^ORCID,Hwang Ji-Hwan²^ORCID,Kim Hwisong¹,Li Chenglei¹,Han Shinhye³,Kim Junwoo¹^ORCID

Affiliation:

1. School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea

2. EchoSensing Inc., Suwon, South Korea

3. Interdiciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea

Funder

National Research Foundation of Korea (NRF) through the Korea Government

Korea Institute of Marine Science and Technology Promotion (KIMST) through the Korea Coast Guard

Integrated Satellite-Based Applications Development for Korea Coast Guard

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10371286.pdf?arnumber=10371286

Reference48 articles.

1. Maritime Surveillance With Undersampled SAR

2. Ship Detection by the RADARSAT SAR: Validation of Detection Model Predictions

3. A Bilateral CFAR Algorithm for Ship Detection in SAR Images

4. An Improved Iterative Censoring Scheme for CFAR Ship Detection With SAR Imagery

5. Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification

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