Abstract
Performance of automatic target recognition from synthetic aperture sonar data is heavily dependent on the complexity of the beamformed imagery. Several mechanisms can contribute to this, including unwanted vehicle dynamics, the bathymetry of the scene, and the presence of natural and manmade clutter. To understand the impact of the environmental complexity on image perception, researchers have taken approaches rooted in information theory, or heuristics. Despite these efforts, a quantitative measure for complexity has not been related to the phenomenology from which it is derived. By using subject matter experts (SMEs) we derive a complexity metric for a set of imagery which accounts for the underlying phenomenology. The goal of this work is to develop an understanding of how several common information theoretic and heuristic measures are related to the SME perceived complexity in synthetic aperture sonar imagery. To achieve this, an ensemble of 10-meter x 10-meter images were cropped from a high-frequency SAS data set that spans multiple environments. The SME's were presented pairs of images from which they could rate the relative image complexity. These comparisons were then converted into the desired sequential ranking using a method first developed by A. Elo for establishing rankings of chess players. The Elo method produced a plausible rank ordering across the broad dataset. The heuristic and information theoretical metrics were then compared to the image rank from which they were derived. The metrics with the highest degree of correlation were those relating to spatial information, e.g. variations in pixel intensity, with an R-squared value of approximately 0.9. However, this agreement was dependent on the scale from which the spatial variation was measured. Results will also be presented for many other measures including lacunarity, image compression, and entropy.
Abstract (translated)
合成孔径声纳数据的自动目标识别性能在很大程度上取决于波束成像图像的复杂性。有几种机制可以促成这一点，包括不必要的车辆动力学，场景的测深，以及自然和人为混乱的存在。为了理解环境复杂性对图像感知的影响，研究人员采用了植根于信息理论或启发式的方法。尽管做了这些努力，但复杂性的量化测量与其衍生的现象学没有关系。通过使用主题专家（SME），我们得出了一组图像的复杂性度量，这些图像考虑了潜在的现象学。这项工作的目的是了解几种常见的信息理论和启发式测量如何与合成孔径声纳图像中的SME感知复杂性相关。为实现这一目标，从跨越多种环境的高频SAS数据集中裁剪出10米x 10米图像的整体。 SME展示了成对的图像，他们可以从中评估相对图像的复杂性。然后使用A.Elo首先开发的用于建立国际象棋选手排名的方法将这些比较转换成期望的顺序排名。 Elo方法在广泛的数据集中产生了合理的排序。然后将启发式和信息理论度量与它们的衍生图像等级进行比较。具有最高相关度的度量是与空间信息有关的度量，例如，像素强度的变化，R平方值约为0.9。然而，该协议取决于测量空间变化的规模。还将提供许多其他测量的结果，包括空隙度，图像压缩和熵。
URL
https://arxiv.org/abs/1808.05279
