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Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
Version 1
: Received: 20 June 2024 / Approved: 20 June 2024 / Online: 20 June 2024 (11:09:12 CEST)
A peer-reviewed article of this Preprint also exists.
Lee, S.; Chung, W. Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation. Appl. Sci. 2024, 14, 6538. Lee, S.; Chung, W. Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation. Appl. Sci. 2024, 14, 6538.
Abstract
In this paper, a novel deep learning approach based on conditional FLOW is proposed for sea clutter augmentation. Sea clutter data augmentation is important for testing detection algorithms for maritime remote sensing and surveillance due to the expensive and time-consuming nature of sea clutter data acquisition. While the conventional parametric methods face challenges in finding appropriate distributions and modeling time correlations of the sea clutter data, the proposed deep learning approach, GCC-FLOW , can learn the data distribution from the data without explicitly defining a mathematical model. Furthermore, unlike the existing generative deep learning approaches, the proposed GCC-FLOW is able to synthesize sea clutter data of arbitrary length with the stable autoregressive structure using conditional FLOW . In addition, the proposed algorithm generates sea clutter data not only with the same characteristics of the training data, but also with the interpolated characteristics of different training data by introducing a global condition variable corresponding to the target characteristic such as sea state. Experimental results demonstrate the effectiveness of the proposed GCC-FLOW in generating sea clutter data of arbitrary length under different sea state conditions.
Keywords
sea clutter; data augmentation; generative algorithm; conditional flow
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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