Selection of HyspIRI optimal band positions for the earth compositional mapping using HyTES data

The National Aeronautics and Space Administration (NASA) has proposed the launch of a new space-borne sensor called HyspIRI (Hyperspectral and Infrared Imager) which will cover the spectral range from 0.4–12 μm. Two instruments will be mounted on HyspIRI platform: 1) a hyperspectral instrument which can sense earth surface between 0.4 and 2.5 μm at 10 nm intervals and 2) a multispectral infrared sensor will acquire images between 3 and 12 μm in eight spectral bands (one in Mid infrared (MIR) and seven in Thermal Infrared (TIR)). The TIR spectral wavebands will be positioned based on their importance in various applications. This study aimed to identify HyspIRI optimal TIR wavebands position for earth compositional mapping. A Genetic Algorithm coupled with the Spectral Angle Mapper (GA-SAM) was used as a spectral bands selector. High dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) emissivity spectra comprised of 202 spectral bands of Cuprite and Death Valley regions were used to select meaningful subsets of bands for earth compositional mapping. The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 50 times. High calibration (> 95%) and validation (> 90%) accuracies were achieved with a limited number (seven) of spectral bands selected by GA-SAM. The knowledge of important band positions will help the scientists of the HyspIRI group to place spectral bands in regions where accuracies of earth compositional mapping can be enhanced

Genetic algorithms for Hyperspectral Range and Operator Selection

A novel genetic algorithm was developed using mathematical operations on spectral ranges to explore spectral operator space and to discover useful mathematical range operations for relating spectral data to reference parameters. For each range, the starting wavelength and length of the range, and a mathematical range operation were selected with a genetic algorithm. Partial least squares (PLS) regression was used to develop models predicting reference variables from the range operations. Reflectance spectra from corn plant canopies were investigated, with proportion of plants (1) with visible tassels and (2) starting to shed pollen as reference data. PLS models developed using the spectral range operator framework had similar fitness than PLS models developed using the full spectrum. This range/operator framework enabled identification of those spectral ranges with most predictive capability and which mathematical operators were most effective in using that predictive capability. Detection of operator locality may have utility in sensor and algorithm design and in developing breeding stock for other algorithms

Novel Analysis of Hyperspectral Reflectance Data for Detecting Onset of Pollen Shed in Maize

Knowledge of pollen shed dynamics in and around seed production fields is critical for ensuring a high yield of genetically pure corn seed. Recently, changes in canopy reflectance using hyperspectral reflectance have been associated with tassel emergence, which is known to precede pollen shed in a predictable manner. Practical application of this remote sensing technology, however, requires a simple and reliable method to evaluate changes in spectral images associated with the onset of tassel emergence and pollen shed. In this study, several numerical methods were investigated for estimating percentage of plants with visible tassels (VT) and percentage of plants that initiated pollen shed (IPS) from remotely sensed hyperspectral reflectance data (397 to 902 nm). Correlation analysis identified regions of the spectra that were associated with tassel emergence and anthesis (i.e., 50% of plants shedding pollen). No single band, however, generated correlations greater than 0.40 for either VT or IPS. Classification using an artificial neural network (ANN) was predictive, correctly classifying 83.5% and 88.3% of the VT and IPS data, respectively. The extensive preprocessing necessary and the black box nature of ANNs, however, rendered analysis of spectral regions difficult using this method. Partial least squares (PLS) analysis yielded models with high predictive capability (R2 of 0.80 for VT and 0.79 for IPS). The PLS coefficients, however, did not exhibit a spectrally consistent pattern. A novel range operator-enabled genetic algorithm (ROE-GA), designed to consider the shape of the spectra, had similar predictive capabilities to the ANN and PLS, but provided the added advantage of allowing information transfer for increased domain knowledge. The ROE-GA analysis is the preferred method to evaluate hyperspectral reflectance data and associate spectral changes to tassel emergence and the onset of pollen shed in corn on a field scale

Spectral Textile Detection in the VNIR/SWIR Band

Dismount detection, the detection of persons on the ground and outside of a vehicle, has applications in search and rescue, security, and surveillance. Spatial dismount detection methods lose e effectiveness at long ranges, and spectral dismount detection currently relies on detecting skin pixels. In scenarios where skin is not exposed, spectral textile detection is a more effective means of detecting dismounts. This thesis demonstrates the effectiveness of spectral textile detectors on both real and simulated hyperspectral remotely sensed data. Feature selection methods determine sets of wavebands relevant to spectral textile detection. Classifiers are trained on hyperspectral contact data with the selected wavebands, and classifier parameters are optimized to improve performance on a training set. Classifiers with optimized parameters are used to classify contact data with artificially added noise and remotely-sensed hyperspectral data. The performance of optimized classifiers on hyperspectral data is measured with Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The best performances on the contact data are 0.892 and 0.872 for Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs), respectively. The best performances on the remotely-sensed data are AUC = 0.947 and AUC = 0.970 for MLPs and SVMs, respectively. The difference in classifier performance between the contact and remotely-sensed data is due to the greater variety of textiles represented in the contact data. Spectral textile detection is more reliable in scenarios with a small variety of textiles

Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in theWater Column of Freshwater Lakes

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS

A machine learning-remote sensing framework for modelling water stress in Shiraz vineyards

Thesis (MA)--Stellenbosch University, 2018.ENGLISH ABSTRACT: Water is a limited natural resource and a major environmental constraint for crop production in viticulture. The unpredictability of rainfall patterns, combined with the potentially catastrophic effects of climate change, further compound water scarcity, presenting dire future scenarios of undersupplied irrigation systems. Major water shortages could lead to devastating loses in grape production, which would negatively affect job security and national income. It is, therefore, imperative to develop management schemes and farming practices that optimise water usage and safeguard grape production. Hyperspectral remote sensing techniques provide a solution for the monitoring of vineyard water status. Hyperspectral data, combined with the quantitative analysis of machine learning ensembles, enables the detection of water-stressed vines, thereby facilitating precision irrigation practices and ensuring quality crop yields. To this end, the thesis set out to develop a machine learning–remote sensing framework for modelling water stress in a Shiraz vineyard. The thesis comprises two components. Component one assesses the utility of terrestrial hyperspectral imagery and machine learning ensembles to detect water-stressed Shiraz vines. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensembles were employed to discriminate between water-stressed and non-stressed Shiraz vines. Results showed that both ensemble learners could effectively discriminate between water-stressed and non-stressed vines. When using all wavebands (p = 176), RF yielded a test accuracy of 83.3% (KHAT = 0.67), with XGBoost producing a test accuracy of 80.0% (KHAT = 0.6). Component two explores semi-automated feature selection approaches and hyperparameter value optimisation to improve the developed framework. The utility of the Kruskal-Wallis (KW) filter, Sequential Floating Forward Selection (SFFS) wrapper, and a Filter-Wrapper (FW) approach, was evaluated. When using optimised hyperparameter values, an increase in test accuracy ranging from 0.8% to 5.0% was observed for both RF and XGBoost. In general, RF was found to outperform XGBoost. In terms of predictive competency and computational efficiency, the developed FW approach was the most successful feature selection method implemented. The developed machine learning–remote sensing framework warrants further investigation to confirm its efficacy. However, the thesis answered key research questions, with the developed framework providing a point of departure for future studies.AFRIKAANSE OPSOMMING: Water is 'n beperkte natuurlike hulpbron en 'n groot omgewingsbeperking vir gewasproduksie in wingerdkunde. Die onvoorspelbaarheid van reënvalpatrone, gekombineer met die potensiële katastrofiese gevolge van klimaatsverandering, voorspel ‘n toekoms van water tekorte vir besproeiingstelsels. Groot water tekorte kan lei tot groot verliese in druiweproduksie, wat 'n negatiewe uitwerking op werksekuriteit en nasionale inkomste sal hê. Dit is dus noodsaaklik om bestuurskemas en boerderypraktyke te ontwikkel wat die gebruik van water optimaliseer en druiweproduksie beskerm. Hyperspectrale afstandswaarnemingstegnieke bied 'n oplossing vir die monitering van wingerd water status. Hiperspektrale data, gekombineer met die kwantitatiewe analise van masjienleer klassifikasies, fasiliteer die opsporing van watergestresde wingerdstokke. Sodoende verseker dit presiese besproeiings praktyke en kwaliteit gewasopbrengs. Vir hierdie doel het die tesis probeer 'n masjienleer-afstandswaarnemings raamwerk ontwikkel vir die modellering van waterstres in 'n Shiraz-wingerd. Die tesis bestaan uit twee komponente. Komponent 1 het die nut van terrestriële hiperspektrale beelde en masjienleer klassifikasies gebruik om watergestresde Shiraz-wingerde op te spoor. Die Ewekansige Woud (RF) en Ekstreme Gradiënt Bevordering (XGBoost) algoritme was gebruik om te onderskei tussen watergestresde en nie-gestresde Shiraz-wingerde. Resultate het getoon dat beide RF en XGBoost effektief kan diskrimineer tussen watergestresde en nie-gestresde wingerdstokke. Met die gebruik van alle golfbande (p = 176) het RF 'n toets akkuraatheid van 83.3% (KHAT = 0.67) behaal en XGBoost het 'n toets akkuraatheid van 80.0% (KHAT = 0.6) gelewer. Komponent twee het die gebruik van semi-outomatiese veranderlike seleksie benaderings en hiperparameter waarde optimalisering ondersoek om die ontwikkelde raamwerk te verbeter. Die nut van die Kruskal-Wallis (KW) filter, sekwensiële drywende voorkoms seleksie (SFFS) wrapper en 'n Filter-Wrapper (FW) benadering is geëvalueer. Die gebruik van optimaliseerde hiperparameter waardes het gelei tot 'n toename in toets akkuraatheid (van 0.8% tot 5.0%) vir beide RF en XGBoost. In die algeheel het RF beter presteer as XGBoost. In terme van voorspellende bevoegdheid en berekenings doeltreffendheid was die ontwikkelde FW benadering die mees suksesvolle veranderlike seleksie metode. Die ontwikkelde masjienleer-afstandwaarnemende raamwerk benodig verder navorsing om sy doeltreffendheid te bevestig. Die tesis het egter sleutelnavorsingsvrae beantwoord, met die ontwikkelde raamwerk wat 'n vertrekpunt vir toekomstige studies verskaf.Master