Sven Schneider

Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors

Hyperspectral data acquired from field-based platforms present new challenges for their analysis, particularly for complex vertical surfaces exposed to large changes in the geometry and intensity of illumination. The use of hyperspectral data to map rock types on a vertical mine face is demonstrated, with a view to providing real-time information for automated mining applications. The performance of two classification techniques, namely, spectral angle mapper (SAM) and support vector machines (SVMs), is compared rigorously using a spectral library acquired under various conditions of illumination. SAM and SVM are then applied to a mine face, and results are compared with geological boundaries mapped in the field. Effects of changing conditions of illumination, including shadow, were investigated by applying SAM and SVM to imagery acquired at different times of the day. As expected, classification of the spectral libraries showed that, on average, SVM gave superior results for SAM, although SAM performed better where spectra were acquired under conditions of shadow. In contrast, when applied to hypserspectral imagery of a mine face, SVM did not perform as well as SAM. Shadow, through its impact upon spectral curve shape and albedo, had a profound impact on classification using SAM and SVM.

Consistency of Measurements of Wavelength Position From Hyperspectral Imagery: Use of the Ferric Iron Crystal Field Absorption at

Several environmental and sensor effects make the determination of the wavelength position of absorption features in the visible near infrared (VNIR) (400-1200 nm) from hyperspectral imagery more difficult than from nonimaging spectrometers. To evaluate this, we focus on the ferric iron crystal field absorption, located at about 900 nm (F900), because it is impacted by both environmental and sensor effects. The consistency with which the wavelength position of F900 can be determined from imagery acquired in laboratory and field settings is evaluated under artificial and natural illumination, respectively. The wavelength position of F900, determined from laboratory imagery, is also evaluated as an indicator of the proportion of goethite in mixtures of crushed rock. Results are compared with those from a high-resolution field spectrometer. Images describing the wavelength position of F900 showed large amounts of spatial variability and contained an artifact-a consistent shift in the wavelength position of F900 to longer wavelengths. These effects were greatly reduced or removed when wavelength position was determined from a polynomial fit to the data, enabling wavelength position to be used to map hematite and goethite in samples of ore and on a vertical surface (a mine face). The wavelength position of F900 from a polynomial fit was strongly positively correlated with the proportion of goethite (R2=0.97). Taken together, these findings indicate that the wavelength position of absorption features from VNIR imagery should be determined from a polynomial (or equivalent) fit to the original data and not from the original data themselves.

\sim

Hyperspectral imagery of a vertical mine face acquired from a field-based platform is used to evaluate the effects of different conditions of illumination on absorption feature parameters wavelength position, depth and width. Imagery was acquired at different times of the day under direct solar illumination and under diffuse illumination imposed by cloud cover. Imagery acquired under direct solar illumination did not show large amounts of variability in any absorption feature parameter; however, imagery acquired under cloud caused changes in absorption feature parameters. These included the introduction of a spurious absorption feature at wavelengths > 2250 nm and a shifting of the wavelength position of specific clay absorption features to longer or shorter wavelengths. Absorption feature depth increased. The spatial patterns of clay absorption in imagery acquired under similar conditions of direct illumination were preserved but not in imagery acquired under cloud. Kaolinite, ferruginous smectite and nontronite were identified and mapped on the mine face. Results were validated by comparing them with predictions from x-ray diffraction and laboratory hyperspectral imagery of samples acquired from the mine face. These results have implications for the collection of hyperspectral data from field-based platforms.

900 nm as an Indicator of Mineralogy

Abstract The ability to map clay minerals on vertical geological surfaces is important from perspectives of stratigraphic mapping and safety. Clay minerals were mapped from hyperspectral imagery using Automated Feature Extraction and their areal coverage estimated on a complex geological surface (a mine pit) by automatically registering hyperspectral to LiDAR data. The area of the mine pit covered by each identified mineral was under- or over-estimated by as much as a factor of 2 when derived from the hyperspectral imagery alone compared to imagery co-registered to LiDAR data. Hyperspectral imagery enabled the identification of clay layers on a mine face as a means of separating geological units of similar visual or spectral characteristics.

Mapping Layers of Clay in a Vertical Geological Surface Using Hyperspectral Imagery: Variability in Parameters of SWIR Absorption Features under Different Conditions of Illumination

A new method is presented which combines a deterministic analytical method and a probabilistic measure to classify rock types on the basis of their hyperspectral curve shape. This method is a supervised learning algorithm using Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function. The OAD covariance function makes use of the properties of the Spectral Angle Mapper (SAM) which is used frequently for classifying hyperspectral data. Results show that it is possible to identify and classify rocks in an ‘One vs. One’ and an ‘One vs. All’ approach using the entire spectral curve (0.35-2.5 microm). The results show an average classification accuracy of 98% and an F-score of 92% for the new method in an ‘One vs. All’ approach. Slightly higher classification accuracy and F-measure for the new method can be achieved for the ‘One vs. One’ binary approach. This paper extends the ideas of the deterministic SAM method to a probabilistic framework and enables data fusion with similar and disparate kinds of sensors. This paper demonstrates a superior classification performance of the new probabilistic method over the classical SAM.

Mapping clay minerals in an open-pit mine using hyperspectral and LiDAR data

A novel spectral unmixing technique is presented which addresses the problem of spectral variability within each endmember class and determines endmember types present in each pixel. The proposed unmixing method is a multi-task framework, based on Multi-task Gaussian Process (MTGP). The Unmixing within a MTGP framework (UMTGP) is different to conventional unmixing approaches in that it assumes that spectral variation exists within each endmember class. Using synthetic and real data, the fractional abundances estimated by the UMTGP are compared with conventional methods such as Fully Constrained Least Squares (FCLS) and Multiple Endmember Spectral Mixture Analysis (MESMA). Hyperspectral data acquired from field-based platforms are used for evaluation because intra-class spectral variability is commonly large in these datasets. The results show that the UMTGP outperforms FCLS in terms of estimating fractional abundance and provides better estimates than MESMA, especially when a small number of endmember spectra for each class are available.

Gaussian Processes with OAD Covariance Function for Hyperspectral Data Classification

There is a strong push within the mining sector to develop and adopt automation technology, including autonomous vehicles such as excavators, trucks and drills. However, for autonomous systems to operate effectively in this domain, new perception capabilities are required to build rich models of a mine. A key element of this is an ability to sense and model the sub-surface geological structure as well as the more traditional robotic models, which typically estimate terrain and obstacles. This paper presents a new automated geological perception system to support autonomous mining. It uses hyperspectral imaging sensors and a supervised learning algorithm to detect and classify geological structures, and ultimately build a rich model of the operating environment. The presented algorithm uses Gaussian Processes (GPs) and an Observation Angle Dependent (OAD) covariance function. Further, the resulting geological model can be improved by fusing data from two hyperspectral scanners which measure different regions of the spectrum. The approach is demonstrated using data from an operational iron-ore mine. Fusion of classification results from the two sensors shows better agreement with ground truth mapping done in the field, compared to results from individual sensors.

Multiple endmember spectral unmixing within a multi-task framework

The ability to automatically classify hyperspectral imagery is of fundamental economic importance to the mining industry. A method of automated multi-class classification based on multi-task Gaussian processes (MTGPs) is proposed for classification of remotely sensed hyperspectral imagery. It is proved that because of the illumination invariance of the hyperspectral curves, the covariance function of the Gaussian process (GPs) has to be non-stationary. To enable multi-class classification of the hyperspectral imagery, a non-stationary multi-task observation angle-dependent covariance function is derived. In order to test MTGP, it was applied to data acquired in the laboratory and also in field. First, the MTGP was applied to hyperspectral imagery acquired under artificial light from samples of rock of known mineral composition. Data from a high-resolution field spectrometer are used to train the GPs. Second, the MTGP was applied to imagery of a vertical rock wall acquired under natural illumination. Spectra from hyperspectral imagery acquired in the laboratory are used to train the GPs. Results were compared with those obtained using the spectral angle mapper (SAM). In laboratory imagery, MTGP outperformed SAM across several metrics, including overall accuracy (MTGP: 0.96–0.98; SAM: 0.91–0.93) and the kappa coefficient of agreement (MTGP: 0.95–0.97; SAM: 0.88–0.91). MTGP applied to hyperspectral imagery of the rock wall gave broadly similar results to those from SAM; however, there were important differences. Some rock types were confused by SAM, but not by MTGP. Comparison of classified imagery with ground truth maps showed that MTGP outperformed SAM.

A geological perception system for autonomous mining

In this paper we use a machine learning algorithm based on Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function to classify hyper spectral imagery for the first time. This paper demonstrates the potential of the GP-OAD method for use in autonomous mining to identify and map geology and mineralogy on a vertical mine face. We discuss the importance of independent training data (i.e. a spectral library) to map any mine face without a priori knowledge. We compare an independent spectral library to other libraries, based on image data, and evaluate their relative performances to distinguish ore bearing zones from waste. Results show that the algorithm yields high accuracies (90%) and F-scores (77%), the best results are achieved when libraries are combined. We also demonstrate mapping of geology using imagery under different conditions of illumination (e.g. shade).