The stability of mine dumps is contingent upon the precise arrangement of spoil piles, taking into account their geological and geotechnical attributes. Yet, on-site characterisation of individual piles poses a formidable challenge. The utilisation of image-based techniques for spoil pile characterisation, employing remotely acquired data through unmanned aerial systems, is a promising complementary solution. Image processing, such as object-based classification and feature extraction, are dependent upon effective segmentation. This study refines and juxtaposes various segmentation approaches, specifically colour-based and morphology-based techniques. The objective is to enhance and evaluate avenues for object-based analysis for spoil characterisation within the context of mining environments. Furthermore, a comparative analysis is conducted between conventional segmentation approaches and those rooted in deep learning methodologies. Among the diverse segmentation approaches evaluated, the morphology-based deep learning segmentation approach, Segment Anything Model (SAM), exhibited superior performance in comparison to other approaches. This outcome underscores the efficacy of incorporating advanced morphological and deep learning techniques for accurate and efficient spoil pile characterisation. The findings of this study contribute valuable insights to the optimisation of segmentation strategies, thereby advancing the application of image-based techniques for the characterisation of spoil piles in mining environments.

翻译：排土场的稳定性取决于考虑其地质与岩土特性的排土堆精确布置。然而，对单个排土堆进行现场表征极具挑战性。利用无人机系统远程获取数据进行基于图像的排土堆表征，是一种具有前景的补充解决方案。图像处理（如面向对象的分类与特征提取）依赖于有效的分割技术。本研究改进并对比了多种分割方法，特别是基于颜色和基于形态学的技术，旨在优化和评估采矿环境中面向对象的排土特征分析途径。此外，本文还对传统分割方法与基于深度学习的方法进行了比较分析。在评估的多种分割方法中，基于形态学的深度学习分割方法——Segment Anything Model（SAM）表现出优于其他方法的性能。这一结果凸显了结合先进形态学与深度学习技术对排土堆进行准确高效表征的有效性。本研究结果为分割策略的优化提供了宝贵见解，从而推动了基于图像技术在采矿环境中排土堆表征中的应用。