Images are particular and well–known instances of spatial big data. Typically spatial data are scale specific and in this paper, we propose mechanisms to effectively address issues of scale in the analysis of images. We focus on spatial data extracted from images using the Discrete Pulse Transform (DPT). The DPT extracts discrete pulses from images at multiple scales that are recognisable as connected components. Traditionally, fractals are used for this purpose, but they fall short as the process underlying fractality is usually either absent or poorly understood. This paper investigates the $Ht$-index (head/tail break) as an alternative, merging ideas from image analysis and spatial statistics. More specifically, we use the $Ht$-index for the analysis of anisotropic point patterns that are obtained from applying the DPT. We propose a multi-level $Ht$-index decomposition in this regard. This is the first mechanism for the DPT enabling an informed partition of the scale-space. The results show that the $Ht$-index is well suited to identify the anisotropic structure location within specific scales and thereby substantially reduces computational costs. We conclude that the use of the $Ht$-index is promising and is well-suited for the further analysis of spatial big data.

图像是空间大数据的特殊且众所周知的实例。通常，空间数据是特定于比例尺的，在本文中，我们提出了一些机制来有效解决图像分析中的比例尺问题。我们专注于使用离散脉冲变换（DPT）从图像中提取的空间数据。DPT从图像中提取出多个比例的离散脉冲，这些脉冲可识别为连接的分量。传统上，分形用于此目的，但由于通常不存在或很少理解分形的过程，因此它们不完善。本文调查了$HŤ$-index（头/尾巴断开）作为替代，将图像分析和空间统计中的思想融合在一起。更具体地说，我们使用$HŤ$-指数，用于分析通过应用DPT获得的各向异性点图案。我们建议多层次$HŤ$索引分解在这方面。这是DPT的第一种机制，可实现对比例空间的明智划分。结果表明$HŤ$指数非常适合于识别特定尺度内的各向异性结构位置，从而大大降低了计算成本。我们得出结论，使用$HŤ$指数很有希望，非常适合对空间大数据进行进一步分析。