The accurate estimation of atmospheric water vapour and the subsequent derivation of surface spectral reflectance from hyperspectral VNIR-SWIR remotely sensed data is important for many applications. A number of algorithms have been developed for estimating water vapour content from remotely sensed hyperspectral data that do not require in-situ measurements. Two algorithms, the Continuum Interpolated Band Ratio (CIBR) and the Atmospheric Precorrected Differential Absorption (APDA) have proven to be highly effective at estimating atmospheric water vapour. Although highly successful, the two methods still exhibit unwanted or spurious results when challenging conditions are encountered. Such conditions include the estimation of atmospheric water vapour over dark targets, when uncorrected atmospheric aerosols are present and over surfaces with complex spectral signatures.A differential absorption method called the Transmittance Slope Ratio (TSR) has been developed that negates these problems. The TSR method is comprised of a weighted mean radiance that is defined between two atmospheric water absorption features which is divided by a reference channel radiance to produce a measurable ratio value. This, is turn, may be related to a reference curve, such that, the TSR value may be expressed as an atmospheric water vapour content. To test the TSR method over real terrains, AVIRIS and HyMap measured hyperspectral radiometric data were used. Three test sites were used in total with each site allowing different aspects of the water vapour estimation to be critically examined. The sites are, Jasper Ridge and Moffett Field in California and Brukunga in South Australia.The TSR method is found to significantly improve estimated atmospheric water vapour over dark targets (with less than 3.5 % error for reflectances as low as 0.5 %), improvement over nonlinear surfaces, and finally, ++ / improvement in water vapour estimation when atmospheric aerosol conditions are not well known. In the final case the TSR method is found to estimate atmospheric water vapour with an error of less than 2 % when a 5 km visibility is assumed to be 25 km. The final result is at least an order of magnitude better than the CIBR and APDA methods.

This research investigates the spectral reflectance characteristics of soil and vegetation using multi-angular and single view hyperspectral data. The question of the thesis is ???How much information can be obtained from multi-angular hyperspectral remote sensing in comparison with single view angle hyperspectral remote sensing of soil and vegetation???? This question is addressed by analysing multi-angular and single view angle hyperspectral remote sensing using data from the field, airborne and space borne hyperspectral sensors. Spectral mapping, spectral indices and Functional Data Analysis (FDA) are used to analyse the data. Spectral mapping has been successfully used to distinguish features of soil and cotton with hyperspectral data. Traditionally, spectral mapping is based on collecting endmembers of pure pixels and using these as training areas for supervised classification. There are, however, limitations in the use of these algorithms when applied to multi-angular images, as the reflectance of a single ground unit will differ at each angle. Classifications using six-class endmembers identified using single angle imagery were assessed using multi-angular Compact High Resolution Imaging Spectrometer (CHRIS) imagery, as well as a set of vegetation indices. The results showed no significant difference between the angles. Low nutrient content in the soil produced lower vegetation index values, and more nutrients increased the index values. This research introduces FDA as an image processing tool for multi-angular hyperspectral imagery of soil and cotton, using basis functions for functional principal component analysis (fPCA) and functional linear modelling. FDA has advantages over conventional statistical analysis because it does not assume the errors in the data are independent and uncorrelated. Investigations showed that B-splines with 20-basis functions was the best fit for multi-angular soil spectra collected using the spectroradiometer and the satellite mounted CHRIS. Cotton spectra collected from greenhouse plants using a spectrodiometer needed 30-basis functions to fit the model, while 20-basis functions were sufficient for cotton spectra extracted from CHRIS. Functional principal component analysis (fPCA) of multi-angular soil spectra show the first fPCA explained a minimum of 92.5% of the variance of field soil spectra for different azimuth and zenith angles and 93.2% from CHRIS for the same target. For cotton, more than 93.6% of greenhouse trial and 70.6% from the CHRIS data were explained by the first fPCA. Conventional analysis of multi-angular hyperspectral data showed significant differences exist between soil spectra acquired at different azimuth and zenith angles. Forward scan direction of zenith angle provides higher spectral reflectance than backward direction. However, most multi-angular hyperspectral data analysed as functional data show no significant difference from nadir, except for small parts of the wavelength of cotton spectra using CHRIS. There is also no significant difference for soil spectra analysed as functional data collected from the field, although there was some difference for soil spectra extracted from CHRIS. Overall, the results indicate that multi-angular hyperspectral data provides only a very small amount of additional information when used for conventional analyses.

Flotation process is mineral processing technique used for separating valuable minerals from the gangue. The research presented in this thesis deals with assessing features that can help in measuring the performance (observing) bitumen flotation process and modeling damage in flotation units. A timely measure of oilsands and process stream contents can be used to observe and control the separation performance. To this end, flotation experiments were conducted and hyperspectral images of the ore and the process stream were taken to determine whether spectral information can predict the bitumen and fines content of ore samples and establish relationship a between these variables and the froth colour. Several features that appear to correspond to clay and quartz were present. Flotation cells are prone to wear damage by particles entrained in the slurry. A wear damage model was developed to predict the damage accumulated over a period of time. Particle image velocimetry experiments were conducted on physical flotation model to understand the flow behavior of the solid particles near the wall of the flotation unit. A preliminary wear test was conducted for qualitative assessment of wear. Recommendations were made for validating the damage model. / Engineering Management

Compressive sensing (CS) is a new sampling theory which allows reconstructing signals using sub-Nyquist measurements. It states that a signal can be recovered exactly from randomly undersampled data points if the signal exhibits sparsity in some transform domain (wavelet, Fourier, etc). Instead of measuring it uniformly in a local scheme, signal is correlated with a series of sensing waveforms. These waveforms are so called sensing matrix or measurement matrix. Every measurement is a linear combination of randomly picked signal components. By applying a nonlinear convex optimization algorithm, the original can be recovered. Therefore, signal acquisition and compression are realized simultaneously and the amount of information to be processed is considerably reduced. Due to its unique sensing and reconstruction mechanism, CS creates a new situation in signal acquisition hardware design as well as software development, to handle the increasing pressure on imaging sensors for sensing modalities beyond visible (ultraviolet, infrared, terahertz etc.) and algorithms to accommodate demands for higher-dimensional datasets (hyperspectral or video data cubes). The combination of CS with traditional optical imaging extends the capabilities and also improves the performance of existing equipments and systems. Our research work is focused on the direct application of compressive sensing for imaging in both 2D and 3D cases, such as infrared imaging, hyperspectral imaging and sum frequency generation microscopy. Data acquisition and compression are combined into one step. The computational complexity is passed to the receiving end, which always contains sufficient computer processing power. The sensing stage requirement is pushed to the simplest and cheapest level. In short, simple optical engine structure, robust measuring method and high speed acquisition make compressive sensing-based imaging system a strong competitor to the traditional one. These applications have and will benefit our lives in a deeper and wider way.

With the increase in atmospheric CO2 concentrations, and the resulting potential for climate change, there has been increasing research devoted to understanding the factors that determine the magnitude of CO2 fluxes and the feedback of ecosystem fluxes on climate. This thesis is an effort to investigate the feasibility of using alternate methods to measure and estimate the CO2 exchange rates in the northern mixed grass prairie. Specifically, the objectives are to evaluate the capability of using ground-level hyperspectral, and satellite-level multispectral data in the estimation of mid-season leaf CO2 exchange rates as measured with a chamber, in and around Grasslands National Park (GNP), Saskatchewan. Data for the first manuscript was collected during June of 2004 (the approximate period for peak greenness for the study area). Spectral reflectance and CO2 exchange measurements were collected from 13 sites in and around GNP. Linear regression showed that the Photochemical Reflectance Index (PRI) calculated from hyperspectral ground-level data explained 46% of the variance seen in the CO2 exchange rates. This indicates that the PRI, which has traditionally been used only in laboratory conditions to predict CO2 exchange, can also be applied at the canopy level in grassland field conditions. <p>The focus of the second manuscript is to establish if the relationship found between ground-level hyperspectral data and leaf CO2 exchange is applicable to satellite-level derived vegetation indices. During June of 2005, biophysical and CO2 exchange measurements were collected from 24 sites in and around GNP. A SPOT satellite image was obtained from June 22, midway through the field data collection. Cubic regression showed that Normalized Difference Vegetation Index (NDVI) explained 46% of the variance observed in the CO2 exchange rates. To our knowledge, this is the first time that a direct correlation between satellite images and leaf CO2 fluxes has been shown within the grassland biome.

In certain industries, quality testing is crucial, to make sure that the components being manufactured do not contain any defects. One method to detect these defects is to heat the specimen being inspected and then to study the cooling process using infrared thermography. The explorations of non-destructive testing using thermography is at an early stage and therefore the purpose of this thesis is to analyse some of the existing techniques and to propose improvements. A test specimen containing several different defects was designed specifically for this thesis. A flash lamp was used to heat the specimen and a high-speed infrared camera was used to study both the spatial and temporal features of the cooling process. An algorithm was implemented to detect anomalies and different parameter settings were evaluated. The results show that the proposed method is successful at finding the searched for defects, and also outperforms one of the old methods.

A recent breakthrough in information theory known as compressive sensing is one component of an ongoing revolution in data acquisition and processing that guides one to acquire less data yet still recover the same amount of information as traditional techniques, meaning less resources such as time, detector cost, or power are required. Starting from these basic principles, this thesis explores the application of these techniques to imaging. The first laboratory example we introduce is a simple infrared camera. Then we discuss the application of compressive sensing techniques to hyperspectral microscopy, specifically Raman microscopy, which should prove to be a powerful technique to bring the acquisition time for such microscopies down from hours to minutes. Next we explore a novel sensing architecture that uses partial circulant matrices as sensing matrices, which results in a simplified, more robust imaging system. The results of these imaging experiments lead to questions about the performance and fundamental nature of sparse signal recovery with partial circulant compressive sensing matrices. Thus, we present the results of a suite of numerical experiments that show some surprising and suggestive results that could stimulate further theoretical and applied research of partial circulant compressive sensing matrices. We conclude with a look ahead to adaptive sensing procedures that allow real-time, interactive optical signal processing to further reduce the resource demands of an imaging system.

With the increase in atmospheric CO2 concentrations, and the resulting potential for climate change, there has been increasing research devoted to understanding the factors that determine the magnitude of CO2 fluxes and the feedback of ecosystem fluxes on climate. This thesis is an effort to investigate the feasibility of using alternate methods to measure and estimate the CO2 exchange rates in the northern mixed grass prairie. Specifically, the objectives are to evaluate the capability of using ground-level hyperspectral, and satellite-level multispectral data in the estimation of mid-season leaf CO2 exchange rates as measured with a chamber, in and around Grasslands National Park (GNP), Saskatchewan. Data for the first manuscript was collected during June of 2004 (the approximate period for peak greenness for the study area). Spectral reflectance and CO2 exchange measurements were collected from 13 sites in and around GNP. Linear regression showed that the Photochemical Reflectance Index (PRI) calculated from hyperspectral ground-level data explained 46% of the variance seen in the CO2 exchange rates. This indicates that the PRI, which has traditionally been used only in laboratory conditions to predict CO2 exchange, can also be applied at the canopy level in grassland field conditions. <p>The focus of the second manuscript is to establish if the relationship found between ground-level hyperspectral data and leaf CO2 exchange is applicable to satellite-level derived vegetation indices. During June of 2005, biophysical and CO2 exchange measurements were collected from 24 sites in and around GNP. A SPOT satellite image was obtained from June 22, midway through the field data collection. Cubic regression showed that Normalized Difference Vegetation Index (NDVI) explained 46% of the variance observed in the CO2 exchange rates. To our knowledge, this is the first time that a direct correlation between satellite images and leaf CO2 fluxes has been shown within the grassland biome.

Imaging spectrometry data or hyperspectral imagery acquired using airborne systems have been used in the geologic community since the early 1980&rsquo / s and represent a mature technology. The solar spectral range 0.4&ndash / 2.5 &amp / #956 / m provides abundant information about hydroxyl-bearing minerals, sulfates and carbonates common to many geologic units and hydrothermal alteration assemblages. Satellite based Hyperion image data is used to implement and test hyperspectral processing techniques to identify alteration minerals and associate the results with the geological setting. Sisorta gold prospect is characterized by porphyry related epithermal and mesothermal alteration zones that are mapped through field studies. Image specific corrections are applied to obtain error free image data. Extensive field mapping and spectroscopic survey are used to identify nine endmembers from the image. Partial unmixing techniques are applied and used to assess the endmembers. Finally the spectral correlation mapper is used to map the endmembers which are kaolinite, dickite, halloysite, illite, montmorillonite and alunite as clay group and hematite, goethite and jarosite as the iron oxide group. The clays and iron oxides are mapped with approximately eighty percent accuracy. The study introduces an image specific algorithm for alteration minerals identification and discusses the outcomes within the geological perspective.

The goal of this dissertation research is to develop and demonstrate a functioning snapshot imaging spectropolarimeter for the long wavelength infrared region of the electromagnetic spectrum (wavelengths from 8-12 microns). Such an optical system will be able to simultaneously measure both the spectral and polarimetric signatures of all the spatial locations/targets in a scene with just a single integration period of a camera. This will be accomplished by combining the use of computed tomographic imaging spectrometry (CTIS) and channeled spectropolarimetry. The proposed system will be the first instrument of this type specifically designed to operate in the long wavelength infrared region, as well as being the first demonstration of such a system using an uncooled infrared focal plane array. In addition to the design and construction of the proof-of-concept snapshot imaging spectropolarimeter LWIR system, the dissertation research will also focus on a variety of methods on improving CTIS system performance. These enhancements will include some newly proposed methods of system design, calibration, and reconstruction aimed at improving the speed of reconstructions allowing for the first demonstration of a CTIS system capable of computing reconstructions in 'real time.'