KEYWORDS: Unmanned aerial vehicles, Mining, Point clouds, 3D modeling, 3D acquisition, Photogrammetry, 3D metrology, Satellites, Data acquisition, Land mines
Mining companies worldwide routinely monitor their excavation activity. Until a few years ago terrestrial measurements, aerial photogrammetry and remote sensing using very high-spatial resolution satellite data were the usual methodologies. In particular, executing precise terrestrial measurements with topographic equipment of Differential GNSS constitutes a time-consuming procedure. Although the absolute precision of individual points is extremely high (mm level), it is challenging to survey large land areas. At the same time, Terrestrial Laser Scanners (TLSs) provide comparable accuracy by collecting millions of points per second, decreasing the surveying time substantially; yet, deploying these sensors inside the quarries continues to be problematic. While costly, with aerial photogrammetry data from large quarry areas is collected at a cm level accuracy. Satellite data present the same pros and cons as aerial photogrammetry in terms of area coverage, accuracy, and cost. The advent of Unmanned Aerial Vehicles (UAVs) and the development of high-accuracy cameras and light-wise LiDAR sensors open new opportunities for the monitoring of quarries. In the present study we evaluate and compare the 3D point clouds derived from high-accuracy UAV cameras to the respective data collected by TLS. An open pit bauxite mine in Greece, monitored in the frame of the m4mining project, is selected as the study area. “Μ4mining” is an EU-funded project that aims at confining the resolution gap between satellite- and UAV-acquired data for mine monitoring. The 3D point clouds derived from UAV flight campaigns and TLS measurements are compared in terms of point density and fidelity of topographic representation. The current work proposes an effective and precise methodology to accurately 3D map a site, using cost-efficient data, acquired by UAV and TLS.
Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a
hyperspectral scene. Most of the spectral-based endmember extraction methods relay on the ability to discriminate
between pixels based on their spectral characteristics and the assumption that pure pixels exist in the image. In some
cases, though pure pixels are available inside image, spectral complexity of the image (e.g. low spectral contrast) makes
it difficult to extract the best endmember candidates from hyperspectral imagery. This paper investigates the use of
statistical convex partitioning (SCP) as a preprocessing tool for endmember extraction. The SCP method comprises three
main steps: 1) partitioning input hyperspectral data set into partitions or so called convex regions using K-mean
clustering algorithm; 2) finding the best candidate endmembers for each convex region; and, 3) comparing and listing of
candidate endmembers extracted from each partition in order of spectral similarity. In order to demonstrate the
performance of the proposed method, the sequential maximum angle convex cone (SMACC) algorithm was used to
extract endmembers of each partition and the results were compared to pixel purity index (PPI). Optimum number of
convex regions as well as the impact of different dimensionality reduction transforms, principal component analysis
(PCA), minimum noise fraction (MNF), and independent component analysis (ICA) were also investigated.
Experimental results on both simulated and real AVIRIS hyperspectral image indicate that SCP is an effective method to
preprocess hyperspectral data spectrally and extract low contrast and similar endmembers effectively.
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