Critical Features Tracking on Triangulated Irregular Networks by a Scale-Space Method

TL;DR

This paper introduces a novel scale-space analysis pipeline for Triangulated Irregular Networks (TINs) that effectively identifies and tracks significant topographic features, outperforming traditional grid-based methods in efficiency, accuracy, and robustness.

Contribution

The work extends scale-space methods from grid-based DEMs to TINs, enabling better feature detection on irregular terrains and boundaries.

Findings

More efficient than grid-based methods

Higher accuracy in feature tracking

Robust to terrain irregularities

Abstract

The scale-space method is a well-established framework that constructs a hierarchical representation of an input signal and facilitates coarse-to-fine visual reasoning. Considering the terrain elevation function as the input signal, the scale-space method can identify and track significant topographic features across different scales. The number of scales a feature persists, called its life span, indicates the importance of that feature. In this way, important topographic features of a landscape can be selected, which are useful for many applications, including cartography, nautical charting, and land-use planning. The scale-space methods developed for terrain data use gridded Digital Elevation Models (DEMs) to represent the terrain. However, gridded DEMs lack the flexibility to adapt to the irregular distribution of input data and the varied topological complexity of different regions. Instead, Triangulated Irregular Networks (TINs) can be directly generated from irregularly distributed point clouds and accurately preserve important features. In this work, we introduce a novel scale-space analysis pipeline for TINs, addressing the multiple challenges in extending grid-based scale-space methods to TINs. Our pipeline can efficiently identify and track topologically important features on TINs. Moreover, it is capable of analyzing terrains with irregular boundaries, which poses challenges for grid-based methods. Comprehensive experiments show that, compared to grid-based methods, our TIN-based pipeline is more efficient, accurate, and has better resolution robustness.