Sildomar T. Monteiro

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.

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Measurement-while-drilling (MWD) data recorded from drill rigs can provide a valuable estimation of the type and strength of the rocks being drilled. Typical MWD sensors include bit pressure, rotation pressure, pull-down pressure, pull-down rate, and head speed. This letter presents an empirical comparison of the statistical performance, ease of implementation, and computational efficiency associated with three machine-learning techniques. A recently proposed method, boosting, is compared with two well-established methods, neural networks and fuzzy logic, used as benchmarks. MWD data were acquired from blast holes at an iron ore mine in Western Australia. The boreholes intersected a number of rock types including shale, iron ore, and banded iron formation. Boosting and neural networks presented the best performance overall. However, from the viewpoint of implementation simplicity and computational load, boosting outperformed the other two methods.

900 nm as an Indicator of Mineralogy

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.

Rock Recognition From MWD Data: A Comparative Study of Boosting, Neural Networks, and Fuzzy Logic

In this paper, a feature selection algorithm based on particle swarm optimization for processing remotely acquired hyperspectral data is presented. Since particle swarm optimization was originally developed to search only continuous spaces, it could not deal with the problem of spectral band selection directly. We propose a method utilizing two swarms of particles in order to optimize simultaneously a desired performance criterion and the number of selected features. The candidate feature sets were evaluated on a regression problem using artificial neural networks to construct nonlinear models of chemical concentration of glucose in soybean crops. Experimental results attesting the viability of the method utilizing real- world hyperspectral data are presented. The particle swarm optimization-based approach presented superior performance in comparison with a conventional feature extraction method.

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

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.

A particle swarm optimization-based approach for hyperspectral band selection

Feature selection is an important step in hyperspectral analysis using machine learning for many applications, in particular to avoid the curse of dimensionality when there is limited available ground truth. This paper presents an approach to select hyperspectral bands using boosting. Boosting decision trees is an efficient and accurate classification technique that has been applied successfully to process hyperspectral data. The learned structure of the trees can provide insight about which bands are more relevant for the classification. We develop a method that takes into account the improvement obtained by each split of the tree ensemble and calculates a relative importance measure of the input features. The method was evaluated using hyperspectral data of rock samples from an iron ore mine in Australia. We show that by retaining only the most relevant features it is possible to reduce the computational load while retaining classification performance.

Particle Swarms for Feature Extraction of Hyperspectral Data

The increasing availability of very-high-resolution (VHR) aerial optical images as well as coregistered Li- DAR data opens great opportunities for improving objectlevel dense semantic labeling of airborne remote sensing imagery. As a result, efficient and effective multisensor fusion techniques are needed to fully exploit these complementary data modalities. Recent researches demonstrated how to process remote sensing images using pre-trained deep convolutional neural networks (DCNNs) at the feature level. In this paper, we propose a decision-level fusion approach using a probabilistic graphical model for the task of dense semantic labeling. Our proposed method first obtains two initial probabilistic labeling predictions from a fully-convolutional neural network and a linear classifier, e.g. logistic regression, respectively. These two predictions are then combined within a higher-order conditional random field (CRF). We utilize graph cut inference to estimate the final dense semantic labeling results. Higher-order CRF modeling helps to resolve fusion ambiguities by explicitly using the spatial contextual information, which can be learned from the training data. Experiments on the ISPRS 2D semantic labeling Potsdam dataset show that our proposed approach compares favorably to the state-of-the-art baseline methods.

Embedded feature selection of hyperspectral bands with boosted decision trees

Attempting to understand and characterize trends in the stock market has been the goal of numerous market analysts, but these patterns are often difficult to detect until after they have been firmly established. Recently, attempts have been made by both large companies and individual investors to utilize intelligent analysis and trading algorithms to identify potential trends before they occur in the market environment, effectively predicting future stock values and outlooks. In this paper, three different classification algorithms will be compared for the purposes of maximizing capital while minimizing risk to the investor. The main contribution of this work is a demonstrated improvement over other prediction methods using machine learning; the results show that tuning support vector machine parameters with particle swarm optimization leads to highly accurate (approximately 95%) and robust stock forecasting for historical datasets.

Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs

Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labelling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.