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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 14
• Main Authors: Song, Juyoung, Kim, Duk-jin, Hwang, Ji-Hwan, Kim, Hwisong, Li, Chenglei, Han, Shinhye, Kim, Junwoo
• Format: Journal Article
• Language: English
• Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
• Subjects: Algorithms; Artificial intelligence; Azimuth; Data augmentation; Defocusing; Detection; Detectors; High resolution; Image enhancement; Image resolution; Mitigation; Motion detection; Moving targets; Phase distortion; Qualitative analysis; Robustness; Sea vessels; Synthetic aperture radar; Target detection; Training; Velocity
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3346171

Along with vessel detection, vessel recognition in high-resolution SAR images was necessary in order to monitor marine vessels effectively; however, the lack of target data and phase defocusing of the target from its velocity limited the recognition performance, especially when using detectors based on artificial intelligence. This study accordingly proposed effective vessel recognition in high-resolution ICEYE spotlight SAR images consecutively using 1) vessel detector robust to defocused moving vessels and 2) mitigation of moving target phase distortion. In order to apply quantitative and qualitative training data enhancement, a target velocity SAR phase refocusing function was developed. The proposed target velocity SAR phase refocusing function generated a defocused SLC image with respect to different target azimuth velocities, which can be used for both training data augmentation and refocusing of velocity-induced phase distortion. Achievement of stable vessel recognition performance was enabled by 1) robust vessel detection on defocused moving vessels and 2) well-focused detected vessel targets, both of which were consecutively applied using the proposed target velocity SAR phase refocusing function. Vessel detection results demonstrated robust performance regardless of vessel motion, and vessel recognition results significantly improved after phase refocusing, both of which were subject to quantitative and qualitative training data enhancement. The performance of the proposed algorithm was analyzed both in terms of phase focusing and velocity estimation. Refocusing performance outperformed that of conventional state-of-the-art autofocusing algorithm, modified Phase Gradient Autofocusing, while azimuth velocity estimation derived the average offset of 0.68 m/s, which was regarded more accurate than previous azimuth velocity estimators based on single-channel SAR image.