استخراج مدل رقومی زمین از ابرنقاط با ارائه یک روش پیش رونده مورفولوژی مبنا

مدل رقومی زمین (DTM)نمایش آماری از سطح پیوسته زمین با استفاده از تعدادی نقطه با مختصات مشخص می باشد. استخراج مدل رقومی زمین به عنوان یکی از مهمترین محصولات فتوگرامتری و سنجش ازدور که پایه بسیاری از پروژه­ های کاربردی است، همواره مدنظر کارشناسان بوده است. با فراهم شدن امکان تهیه نقاط با مختصات سه بعدی و دقت بالا از سطح زمین با استفاده از لیدار و یا تناظریابی چگال از تصاویر رقومی هوایی، زمینه دستیابی به مدل رقومی سطحی (DSM) با دقت مکانی بالافراهم گشت. با این حال رسیدن از مدل رقومی سطحی به مدل رقومی زمین همچنان موضوعی پرچالش در نظر محققان است. در این مقاله روشی کاربردی در راستای استخراج مدل رقومی زمین با استفاده از ابرنقاط طراحی و پیاده سازی شد. در این روش طی دو روند مجزا و با درنظرگیری خصوصیات ساختاری محیط، عوارض غیرزمینی استخراج شده و پس از تلفیق آنها نتیجه نهایی حاصل گشته است. به طوریکه ابتدا یک روند مورفولوژی مبنای پیشرونده طراحی شد که در آن طی افزایش تدریجی ابعاد المان ساختاری عوارض غیرزمینی شناسایی شدند. روند دوم بر مبنای ژئودزیک مورفولوژی و افزایش تدریجی المان ارتفاعی بوده است. بهره گیری از دو روند به دلیل پوشش های متنوع، ناهمواری های متفاوت و عوارض بسیار متنوع مناطق مختلف صورت گرفت تا عملکرد روش پیشنهادی افزایش یابد. پس از حذف عوارض شناسایی شده و بازیابی مناطق از دست رفته از طریق درون یابی مکعبی، مدل رقومی نهایی حاصل گشت. جهت ارزیابی از ابرنقاط حاصل از تناظریابی متراکم تصاویر هوایی رقومی و همینطور ابرنقاط لیدار بهره گرفته شد. نتایج ارزیابی  در 7 ناحیه مطالعاتی نشان از خطای RMSE متوسط 68/0 متر در استخراج مدل رقومی زمین و متوسط 85/4% در شناسایی عوارض غیرزمینی داشت

Airborne LiDAR Point Cloud Filtering Algorithm Based on Supervoxel Ground Saliency

Airborne laser scanning (ALS) is able to penetrate sparse vegetation to obtain highly accurate height information on the ground surface. LiDAR point cloud filtering is an important prerequisite for downstream tasks such as digital terrain model (DTM) extraction and point cloud classification. Aiming at the problem that existing LiDAR point cloud filtering algorithms are prone to errors in complex terrain environments, an ALS point cloud filtering method based on supervoxel ground saliency (SGSF) is proposed in this paper. Firstly, a boundary-preserving TBBP supervoxel algorithm is utilized to perform supervoxel segmentation of ALS point clouds, and multi-directional scanning strip delineation and ground saliency computation are carried out for the clusters of supervoxel point clouds. Subsequently, the energy function is constructed by introducing the ground saliency and the optimal filtering plane of the supervoxel is solved using the semi-global optimization idea to realize the effective distinction between ground and non-ground points. Experimental results on the ALS point cloud filtering dataset openGF indicate that, compared to state-of-the-art surface-based filtering methods, the SGSF algorithm achieves the highest average values across various terrain conditions for multiple evaluation metrics. It also addresses the issue of recessed structures in buildings being prone to misclassification as ground points

A comparison of open-source LiDAR filtering algorithms in a mediterranean forest environment

Light detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15° were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them

Optimising mobile laser scanning for underground mines

Despite several technological advancements, underground mines are still largely relied on visual inspections or discretely placed direct-contact measurement sensors for routine monitoring. Such approaches are manual and often yield inconclusive, unreliable and unscalable results besides exposing mine personnel to field hazards. Mobile laser scanning (MLS) promises an automated approach that can generate comprehensive information by accurately capturing large-scale 3D data. Currently, the application of MLS has relatively remained limited in mining due to challenges in the post-registration of scans and the unavailability of suitable processing algorithms to provide a fully automated mapping solution. Additionally, constraints such as the absence of a spatial positioning network and the deficiency of distinguishable features in underground mining spaces pose challenges in mobile mapping. This thesis aims to address these challenges in mine inspections by optimising different aspects of MLS: (1) collection of large-scale registered point cloud scans of underground environments, (2) geological mapping of structural discontinuities, and (3) inspection of structural support features. Firstly, a spatial positioning network was designed using novel three-dimensional unique identifiers (3DUID) tags and a 3D registration workflow (3DReG), to accurately obtain georeferenced and coregistered point cloud scans, enabling multi-temporal mapping. Secondly, two fully automated methods were developed for mapping structural discontinuities from point cloud scans – clustering on local point descriptors (CLPD) and amplitude and phase decomposition (APD). These methods were tested on both surface and underground rock mass for discontinuity characterisation and kinematic analysis of the failure types. The developed algorithms significantly outperformed existing approaches, including the conventional method of compass and tape measurements. Finally, different machine learning approaches were used to automate the recognition of structural support features, i.e. roof bolts from point clouds, in a computationally efficient manner. Roof bolts being mapped from a scanned point cloud provided an insight into their installation pattern, which underpinned the applicability of laser scanning to inspect roof supports rapidly. Overall, the outcomes of this study lead to reduced human involvement in field assessments of underground mines using MLS, demonstrating its potential for routine multi-temporal monitoring