Abstract
This paper presents a practical framework for the integration of unmanned aerial vehicle (UAV) based photogrammetry and terrestrial laser scanning (TLS) with application to open-pit mine areas, which includes UAV image and TLS point cloud acquisition, image and cloud point processing and integration, object-oriented classification and three-dimensional (3D) mapping and monitoring of open-pit mine areas. The proposed framework was tested in three open-pit mine areas in southwestern China. (1) With respect to extracting the conjugate points of the stereo pair of UAV images and those points between TLS point clouds and UAV images, some feature points were first extracted by the scale-invariant feature transform (SIFT) operator and the outliers were identified and therefore eliminated by the RANdom SAmple Consensus (RANSAC) approach; (2) With respect to improving the accuracy of geo-positioning based on UAV imagery, the ground control points (GCPs) surveyed from global positioning systems (GPS) and the feature points extracted from TLS were integrated in the bundle adjustment, and three scenarios were designed and compared; (3) With respect to monitoring and mapping the mine areas for land reclamation, an object-based image analysis approach was used for the classification of the accuracy improved UAV ortho-image. The experimental results show that by introduction of TLS derived point clouds as GCPs, the accuracy of geo-positioning based on UAV imagery can be improved. At the same time, the accuracy of geo-positioning based on GCPs form the TLS derived point clouds is close to that based on GCPs from the GPS survey. The results also show that the TLS derived point clouds can be used as GCPs in areas such as in mountainous or high-risk environments where it is difficult to conduct a GPS survey. The proposed framework achieved a decimeter-level accuracy for the generated digital surface model (DSM) and digital orthophoto map (DOM), and an overall accuracy of 90.67% for classification of the land covers in the open-pit mine.
Highlights
With the advent and availability of accurate and miniature global navigation satellite systems (GNSS)and inertial measurement units (IMUs), together with the availability of quality consumer-grade digital cameras and other miniature sensors, the unmanned aerial vehicle (UAV) technique has been developing rapidly in the civilian community [1,2,3]
In order to improve the accuracy of geo-positioning based on UAV imagery in the three mine areas, the extracted feature points in the 3D point clouds obtained from the terrestrial laser scanning (TLS) are used as a supplement to the ground control points (GCPs) in the bundle adjustment, and three scenarios for integration of both point clouds from 3D TLS and GCPs from a global positioning systems (GPS) survey were designed as follows
In the proposed framework, (1) in order to extract the conjugate points of the stereo pair of UAV images and those points between TLS point clouds and UAV images, the feature points were first extracted by the scale-invariant feature transform (SIFT) operator and the outliers were identified and eliminated by the RANdom
Summary
Inertial measurement units (IMUs), together with the availability of quality consumer-grade digital cameras and other miniature sensors, the unmanned aerial vehicle (UAV) technique has been developing rapidly in the civilian community [1,2,3]. The UAV systems, which are based on multiple low-cost and conventional platforms [5,6,7,8] and are equipped with multiple flexible and efficient sensors [9,10,11], are able to acquire high-resolution images for photogrammetric and remote sensing applications. UAV remote sensing is a flexible and efficient way of obtaining high-resolution images providing accurate information from low altitudes with less interference from clouds
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