Synthetic Imagery for Spatial Resolution Selection in Maritime Remote Sensing

In recent years, deep neural-networks have gained popularity in maritime detection problems. Successes in deep-learning have been due, partially, to the controlled and constrained nature of the training dataset. However, remote sensing data are highly variable, highly unconstrained, and lack both the quality and quantity of labeled and curated training samples usually required for current state-of-the-art approaches. In this paper we address a lack of class coherency across datasets at varying spatial resolutions by introducing a large 42-class synthetic dataset, Maritime Vessels at Varying Resolutions (MVVR-42). We leverage MVVR-42 in our experimentation, taking advantage of the ability to easily render imagery at varying resolutions and augmenting the training set to produce data points to aid in sensor selection for remote systems. Using state-the-art detection models like YOLO and FasterRCNN, we explore the effect of spatial resolution on performance in ship detection tasks.