A probablistic framework for classification and fusion of remotely sensed hyperspectral data

Abstract

Reliable and accurate material identification is a crucial component underlying higher-level autonomous tasks within the context of autonomous mining. Such tasks can include exploration, reconnaissance and guidance of machines (e.g. autonomous diggers and haul trucks) to mine sites. This thesis focuses on the problem of classification of materials (rocks and minerals) using high spatial and high spectral resolution (hyperspectral) imagery, collected remotely from mine faces in operational open pit mines. A new method is developed for the classification of hyperspectral data including field spectra and imagery using a probabilistic framework and Gaussian Process regression. The developed method uses, for the first time, the Observation Angle Dependent (OAD) covariance function to classify high-dimensional sets of data. The performance of the proposed method of classification is assessed and compared to standard methods used for the classification of hyperspectral data. This is done using a staged experimental framework. First, the proposed method is tested using high-resolution field spectrometer data acquired in the laboratory and in the field. Second, the method is extended to work on hyperspectral imagery acquired in the laboratory and its performance evaluated. Finally, the method is evaluated for imagery acquired from a mine face under natural illumination and the use of independent spectral libraries to classify imagery is explored. A probabilistic framework was selected because it best enables the integration of internal and external information from a variety of sensors. To demonstrate advantages of the proposed GP-OAD method over existing, deterministic methods, a new framework is proposed to fuse hyperspectral images using the classified probabilistic outputs from several different images acquired of the same mine face. This method maximises the amount of information but reduces the amount of data by condensing all available information into a single map. Thus, the proposed fusion framework removes the need to manually select a single classification among many individual classifications of a mine face as the `best' one and increases the classification performance by combining more information. The methods proposed in this thesis are steps forward towards an automated mine face inspection system that can be used within the existing autonomous mining framework to improve productivity and efficiency. Last but not least the proposed methods will also contribute to increased mine safety