# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
###### 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. Whereas the conventional parametric methods face challenges in finding appropriate distributions and modeling time correlations of 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. sea clutter; data augmentation; generative algorithm; conditional flow +
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In addition, the memoryless nonlinear transform (MNLT) method [21], which allows the generation of samples with a specific correlation function, was used to model the spatiotemporal correlation. Although these parametric methods based on mathematical modeling have shown success in finding an appropriate distribution to fit the sea clutter data under ideal assumptions, they are still insufficient to model the full range of geometry and sea conditions in practice, especially for the time correlation. In addition, it is difficult to properly determine the model parameters because the amount of measured data in a given sea state to fit the parameters is always limited due to the time-varying nature of the sea state [22]. Recently, the use of deep learning for data augmentation has become widespread in many fields. The advantages of deep learning approaches in data augmentation are that data with approximately the same statistical properties as sample data, such as distribution and correlation, can be generated without explicit mathematical models, and several generative techniques can be used to generate unlearned data with interpolated features [23; 24]. There are a few attempts to generate sea clutter data using deep learning [25; 26]. In [25], a generative adversarial network (GAN) with two generators that independently generate the real and imaginary components of sea clutter data is proposed, and in [26], a so-called auxiliary classifier GAN (AC-VAEGAN) is proposed to generate one-dimensional (1-D) sea-land clutter amplitude data to deal with imbalanced datasets in classification tasks. These existing methods are based on one-dimensional data generation, and therefore multi-dimensional structures such as real-imaginary dependency are difficult to implement. More importantly, since GAN independently generates data block by block, the length of the coherently generated data is limited to the length of the training data. However, the non-blockwise generative algorithms based on RNN and its variants tend to be inaccurate as the length of the sample sequence increases [27; 28]. Therefore, it is desirable to design a deep generative algorithm capable of generating arbitrarily long multidimensional data from relatively short length of training data. This paper proposes a novel sea clutter data augmentation scheme based on conditional FLOW [29], a variant of FLOW [30], which is one of the most robust deep generative methods. The proposed algorithm, named globally conditioned FOLW (GCC-FLOW), is a modified version of conditional FLOW that introduces a global condition variable to reflect the sea state condition.