Indiana University-Purdue University Indianapolis (IUPUI) / Water is an essential resource for life on Earth, and monitoring its quality is an important task for mankind. However, the amount of water quality data collected by the traditional method is insufficient for the conservation and sustainable management of this important resource. This challenge will be exacerbated by increasing harmful algal blooms at the global scale. To fill this gap, Earth Observations (EO) have been proposed to help stakeholders make their decisions, but the use of EO for monitoring inland water quality is still in development. In this context, the main objective of this study was to improve the estimation of cyanobacteria via remote sensing data. To achieve this goal, the water type classification was first used to identify the dominant optically active constituents within aquatic environments. This information is crucial for understanding the optical properties of inland waters and selecting the best remote sensing algorithm for specific optical water types. The next research question was to develop a universal structure for retrieval of the inherent optical properties of several important aquatic systems around the world, which can be used as a corner stone for developing a globally applicable remote sensing algorithm. The third research topic of this dissertation is about removing the interference of chlorophyll-a with the absorption strength at 620 nm where phycocyanin exhibits its diagnostic absorption so that the estimation of phycocyanin concentration can be improved. Despite the novelty of the proposed remote sensing algorithms which are able to accommodate distinct water optical properties, there are abundant opportunities for improving the parameterization of the proposed models to retrieve inland water quality and optical properties when a global database of optical and water quality measurements is available. Considering the current advancement in spaceborne technology and the existence of a coordinate effort for global calibration and validation of remote sensing algorithms for monitoring inland waters, there is a high potential for operational assessment of harmful cyanobacterial blooms using the remote sensing algorithms proposed in this dissertation.

In this dissertation, novel techniques for hyperspectral classification and signal reconstruction from random projections are presented. A classification paradigm designed to exploit the rich statistical structure of hyperspectral data is proposed. The proposed framework employs the local Fisher’s discriminant analysis to reduce the dimensionality of the data while preserving its multimodal structure, followed by a subsequent Gaussianmixture- model or support-vector-machine classifier. An extension of this framework in a kernel induced space is also studied. This classification approach employs a maximum likelihood classifier and dimensionality reduction based on a kernel local Fisher’s discriminant analysis. The technique imposes an additional constraint on the kernel mapping—it ensures that neighboring points in the input space stay close-by in the projected subspace. In a typical remote sensing flow, the sender needs to invoke an appropriate compression strategy for downlinking signals (e.g., imagery to a base station). Signal acquisition using random projections significantly decreases the sender-side computational cost, while preserving useful information. In this dissertation, a novel class-dependent hyperspectral image reconstruction strategy is also proposed. The proposed method employs statistics pertinent to each class as opposed to the average statistics estimated over the entire dataset, resulting in a more accurate reconstruction from random projections. An integrated spectral-spatial model for signal reconstruction from random projections is also developed. In this approach, spatially homogeneous segments are combined with spectral pixel-wise classification results in the projected subspace. An appropriate reconstruction strategy, such as compressive projection principal component analysis (CPPCA), is employed individually in each category based on this integrated map. The proposed method provides better reconstruction performance as compared to traditional methods and the class-dependent CPPCA approach.

The High Efficiency HyperSpectral Imager (HEHSI) is a Fourier Transform hyperspectral imager based on a Sagnac interferometer. This thesis research concentrates on the design upgrade and calibration of HEHSI from a proof of concept instrument to a prototype field instrument. Stability is enhanced by removing degrees of freedom and alignment is enhanced by providing for in-situ adjustments. The use of off the shelf components allows for reduced development time and cost constraints. HEHSI is capable of multiple configurations to accommodate sensors and optics with specialized capabilities for multiple wavelength ranges and viewing conditions. With a spectral response of 400 to 1000 nanometers in the visible and very near IR as well as 900 to 1700nm in the Near IR. Creation and use of a real time feedback alignment utility allow quantifiable signal comparison and image alignment. Advances allow for HEHSI to remain aligned during data collection sessions and confirmation of alignment through quantitative measures.

Hyperspectral imagery is a highly dimensional type of data resulting in high computational costs during analysis. Band selection aims to reduce the original hyperspectral image to a smaller subset that reduces these costs while preserving the maximum amount of spectral information within the data. This thesis explores various types of band selection techniques used in hyperspectral image processing. Modifying Neural network-based techniques and observing the effects on the band selection process due to the change in network architecture or objective are of particular focus in this thesis. Herein, a generalized neural network-based band selection technique is developed and compared to state-of-the-art algorithms that are applied to a unique dataset and the Pavia City Center dataset where the subsequent selected bands are fed into a classifier to gather comparison results.

It is a popular practice in the remote-sensing community to apply principal component analysis (PCA) on a higher-dimensional feature space to achieve dimensionality-reduction. Several factors that have led to the popularity of PCA include its simplicity, ease of use, availability as part of popular remote-sensing packages, and optimal nature in terms of mean square error. These advantages have prompted the remote-sensing research community to overlook many limitations of PCA when used as a dimensionality-reduction tool for classification and target-detection applications. This thesis addresses the limitations of PCA when used as a dimensionality-reduction technique for extracting discriminating features from hyperspectral data. Theoretical and experimental analyses are presented to demonstrate that PCA is not necessarily an appropriate feature-extraction method for high-dimensional data when the objective is classification or target-recognition. The influence of certain data-distribution characteristics, such as within-class covariance, between-class covariance, and correlation on PCA transformation, is analyzed in this thesis. The classification accuracies obtained using PCA features are compared to accuracies obtained using other feature-extraction methods like variants of Karhunen-Loève transform and greedy search algorithms on spectral and wavelet domains. Experimental analyses are conducted for both two-class and multi-class cases. The classification accuracies obtained from higher-order PCA components are compared to the classification accuracies of features extracted from different regions of the spectrum. The comparative study done on the classification accuracies that are obtained using above feature-extraction methods, ascertain that PCA may not be an appropriate tool for dimensionality-reduction of certain hyperspectral data-distributions, when the objective is classification or target-recognition.

Influence of nitrogen (N) deficiency on castor cv. ‘Hale’ growth, physiology, and leaf reflectance properties were investigated. Treatments imposed were complete Hoagland’s nutrient solution (control, 100N), reduced N to 20% of the control (20N) and withheld N from the solution (0N) from 34 to 66 days after sowing (DAS) in 12-L pots grown out doors. N deficiency significantly reduced leaf area, chlorophyll and photosynthesis resulting lower total biomass. Leaf and stem growth rates were more sensitive to leaf N concentration than photosynthesis and leaf addition rates. N deficiency stress increased leaf reflectance at R555 and R715 nm and caused a red-edge shift to shorter wavelengths. Reflectance ratios of R455/R605 and R505/R605 nm was highly correlated with leaf N on weight (r2 = 0.93) and area-based (r2 = 0.90) estimations, respectively. Similarly, reflectance ratio R635/R505 was highly correlated with chlorophylls (r2 = 0.94). The N-specific wavebands and functional relationships between leaf N and growth and developmental processes would be useful for rapid and non-destructive estimation of leaf N and growth rates of castor.

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Over the past decade, graphene/nanomaterial hybrids have gained a great interest in various applications due to their unique optical properties. This work explores lanthanide doped upconverting nanoparticles (UCNPs)/graphene hybrid nanomaterials. Here, core/shell structures comprising β-NaGdF4:Y b3+(20%),Er3+(2%)@NaGdF4 and α-NaGdF4:Y b3+(20%), Er3+(2%)@NaGdF4 with oleate as capping agent were synthesized and characterized. The choice of lanthanide ions (Yb3+ and Er3+) and their concentrations plays an important role to make these nanoparticles undergo two optical processes (upcoversion and downshifting) capable to convert near-infrared excitation to visible and near-infrared emission. In order to make hybrid systems, these nanoparticles were combined with graphene films. The morphology and the optical behavior of the hybrid samples were studied by microscope and hyperspectral imaging. The multi-energy sublevels from the 4f electronic configuration of lanthanides, their long excited state lifetime and the high carrier mobility of the graphene expected to open an exciting possibility of interaction, however, UCNPs/Graphene hybrid nanomaterial exhibits a minimal response when subjected to 980 nm laser illumination.

The need for an efficient dimensionality reduction technique has remained a critical challenge for effective analysis of hyperspectral data for vegetation applications. Discrete wavelet transform (DWT), through multiresolution analysis, offers oppurtunities both to reduce dimension and convey information at multiple spectral scales. In this study, we investigated the utility of the Haar DWT for AVIRIS hyperspectral data analysis in three different applications (1) classification of three pine species (Pinus spp.), (2) estimation of leaf area index (LAI) using an empirically-based model, and (3) estimation of LAI using a physically-based model. For pine species classification, different sets of Haar wavelet features were compared to each other and to calibrated radiance. The Haar coefficients selected by stepwise discriminant analysis provided better classification accuracy (74.2%) than the original radiance (66.7%). For empirically-based LAI estimation, the models using the Haar coefficients explained the most variance in observed LAI for both deciduous plots (cross validation R² (CV-R²) = 0.79 for wavelet features vs. CV-R² = 0.69 for spectral bands) and all plots combined (CV R² = 0.71 for wavelet features vs. CV-R² = 0.50 for spectral bands). For physically-based LAI estimation, a look-up-table (LUT) was constructed by a radiative transfer model, DART, using a three-stage approach developed in this study. The approach involved comparison between preliminary LUT reflectances and image spectra to find the optimal set of parameter combinations and input increments. The LUT-based inversion was performed with three different datasets, the original reflectance bands, the full set of the wavelet extracted features, and the two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R² = 0.77) than the original spectral bands (RMSE = 0.69, R² = 0.42). This study has demonstrated that the application of the discrete wavelet transform can provide more accurate species discrimination within the same genus than the original hyperspectral bands and can improve the accuracy of LAI estimates from both empirically- and physically-based models. / Ph. D.