Unmanned aerial vehicle (UAV) based hyperspectral imaging is a relatively new remote sensing technology. Near-earth spectroscopy from UAV-hyperspectral systems addresses the limitations of resolution, signal-to-noise ratio, and cloud cover from traditional satellite or airborne platforms. The technology is increasingly being used to obtain fast and accurate information. Recently developed methods for data acquisition and pre-processing have mostly focused on agriculture and forestry applications. This paper addresses the challenges in the methods of sensor calibration, data acquisition, radiometric correction for illumination variation, mosaicking and geometric correction for UAV-hyperspectral imaging of highly heterogeneous environments, such as swamps. A pre-processing workflow was developed to address the specific issues of monitoring the heterogenous distribution of the swamp vegetation and assess its condition. The workflow was used to produce reflectance products and vegetation indices. The developed framework would be useful in remote environments where satellite or airborne hyperspectral observations are limited due to the presence of spectrally complex environment.

Roof bolts are commonly used to provide structural support in underground mines. Frequent and automated assessment of roof bolt is critical to closely monitor any change in the roof conditions while preventing major hazards such as roof fall. However, due to challenging conditions at mine sites such as sub-optimal lighting and restrictive access, it is difficult to routinely assess roof bolts by visual inspection or traditional surveying. To overcome these challenges, this study presents an automated method of roof bolt identification from 3D point cloud data, to assist in spatio-temporal monitoring efforts at mine sites. An artificial neural network was used to classify roof bolts and extract them from 3D point cloud using local point descriptors such as the proportion of variance (POV) over multiple scales, radial surface descriptor (RSD) over multiple scales and fast point feature histogram (FPFH). Accuracy was evaluated in terms ofprecision, recall and quality metric generally used in classification studies. The generated results were compared against other machine learning algorithms such as weighted k-nearest neighbours (k-NN), ensemble subspace k-NN, support vector machine (SVM) and random forest (RF), and was found to be superior by up to 8% in terms of the achieved quality metric.

Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability of global navigation satellite system (GNSS) signals. Consequently, applications that require georeferenced point cloud or coregistered multitemporal point clouds such as detecting changes, monitoring deformations, tracking mine logistics, measuring roadway convergence rate and evaluating construction performance become challenging. Current mapping practices largely include a manual selection of discernable reference points in laser scans for georeferencing and coregistration which is often time-consuming, arduous and error-prone. Moreover, challenges in obtaining a sensor positioning framework, the presence of structurally symmetric layouts and highly repetitive features (such as roof bolts) makes the multitemporal scans difficult to georeference and coregister. This study aims at overcoming these practical challenges through development of three-dimensional unique identifiers (3DUIDs) and a 3D registration (3DReG) workflow. Field testing of the developed approach in an underground coal mine has been found effective with an accuracy of 1.76 m in georeferencing and 0.16 m in coregistration for a scan length of 850 m. Additionally, automatic extraction of mine roadway profile has been demonstrated using 3DUID which is often a compliant and operational requirement for mitigating roadway related hazards that includes roadway convergence rate, roof/rock falls, floor heaves and vehicle clearance for collision avoidance. Potential applications of 3DUID include roadway profile extraction, guided automation, sensor calibration, reference targets for a routine survey and deformation monitoring.