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Spectroradiometer data (350 – 2500 nm) were acquired in late summer 1999 over various forest sites in Appomattox Buckingham State Forest, Virginia, to assess the spectral differentiability among six major forestry tree species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), shortleaf pine (Pinus echinata), scarlet oak (Quercus coccinea), white oak (Quercus alba), and yellow poplar (Liriodendron tulipifera). Data were smoothed using both moving (9-point) and static (10 nm average) filters and curve shape was determined using first and second differences of resultant data sets. Stepwise discriminant analysis decreased the number of independent variables to those significant for spectral discrimination at -level of 0.0025. Canonical discriminant analysis and a normal discriminant analysis were performed on the data sets to test separability between and within taxonomic groups. The hardwood and pine groups were shown to be highly differentiable with a 100% cross-validation accuracy. The three pines were less differentiable, with cross-validation results varying from 61.64% to 84.25%, while spectral separability among the three hardwood species showed more promise, with classification accuracies ranging from 78.36% to 92.54%. The second difference of the 9-point weighted average filter was the most effective data set, with accuracies ranging from 84.25% to 100.00% for the separability tests. Overall, variables needed for spectral discrimination were well distributed across the 350 nm to 2500 nm spectral range, indicating the usefulness of the whole wavelength range for discriminating between taxonomic groups and among species. Derivative analysis was shown to be effective for between and within group spectral discrimination, given that the data were smoothed first. Given the caveat of the limited species diversity examined, results of this study indicate that leaf-on hyperspectral remotely sensed data will likely afford spectral discrimination between hardwoods and softwoods, while discrimination within taxonomic groups might be more problematic. / Master of Science

Electromagnetic radiation reflectance increases dramatically around 700 nm for vegetation. This increase in reflectance is known as the vegetation red edge. The NDVI (Normalized Difference Vegetation index) is an imaging technique for quantifying red edge contrast for the identification of vegetation. This imaging technique relies on reflectance values for radiation with wavelength equal to 680 nm and 830 nm. The imaging systems required to obtain this precise reflectance data are commonly space-based; limiting the use of this technique due to satellite availability and cost. This thesis presents a robust and inexpensive new terrestrial-based method for identifying the vegetation red edge. This new technique does not rely on precise wavelengths or narrow wavelength bands and instead applies the NDVI to the visible and NIR (near infrared) spectrums in toto. The measurement of vegetation fluorescence has also been explored, as it is indirectly related to the efficiency of photochemistry and heat dissipation and provides a relative method for determining vegetation health. The imaging methods presented in this thesis represent a unique solution for the real time monitoring of vegetation growth and senesces and the determination of qualitative vegetation health. A single, inexpensive system capable of field and greenhouse deployment has been developed. This system allows for the early detection of variations in plant growth and status, which will aid production of high quality horticultural crops. / Master of Science

During their growth, crops may experience a variety of health issues, which often lead to a reduction in crop yield. In order to avoid financial loss and sustain crop survival, it is imperative for farmers to detect and treat crop health issues. Interest in the use of unmanned aerial vehicles (UAVs) for precision agriculture has continued to grow as the cost of these platforms and sensing payloads has decreased. The increase in availability of this technology may enable farmers to scout their fields and react to issues more quickly and inexpensively than current satellite and other airborne methods. In the work of this thesis, methods have been developed for applications of UAV remote sensing using visible spectrum and multispectral imagery. An algorithm has been developed to work on a server for the remote processing of images acquired of a crop field with a UAV. This algorithm first enhances the images to adjust the contrast and then classifies areas of the image based upon the vigor and greenness of the crop. The classification is performed using a support vector machine with a Gaussian kernel, which achieved a classification accuracy of 86.4%. Additionally, an analysis of multispectral imagery was performed to determine indices which correlate with the health of corn crops. Through this process, a method for correcting hyperspectral images for lighting issues was developed. The Normalized Difference Vegetation Index values did not show a significant correlation with the health, but several indices were created from the hyperspectral data. Optimal correlation was achieved by using the reflectance values for 740 nm and 760 nm wavelengths, which produced a correlation coefficient of 0.84 with the yield of corn. In addition to this, two algorithms were created to detect stink bugs on crops with aerial visible spectrum images. The first method used a superpixel segmentation approach and achieved a recognition rate of 93.9%, although the processing time was high. The second method used an approach based upon texture and color and achieved a recognition rate of 95.2% while improving upon the processing speed of the first method. While both methods achieved similar accuracy, the superpixel approach allows for detection from higher altitudes, but this comes at the cost of extra processing time. / Master of Science

Les maladies du bois de la vigne sont responsables de pertes économiques importantes pour la filière viticole. Ces maladies d'origine fongique se manifestent notamment par une dégradation de la partie boisée du matériel végétal et par l'apparition erratique de symptômes caractéristiques sur la partie foliaire. Cette thèse est dédiée à l'étude de ces maladies (principalement l'esca) à l'aide de deux capteurs imageurs en proxidétection.La question de la détection des symptômes visibles est tout d'abord abordée à l'aide d'un capteur couleur RVB permettant d'acquérir une image par pied de manière automatique ou semi-automatique. La reconnaissance des symptômes est abordée en deux étapes, d'abord en considérant la classification à l'échelle de la feuille puis la détection à l'échelle du pied. La particularité de cette étude est l'inclusion de facteurs confondants dans le problème de classification, tirant partie de l'information de forme des symptômes de l'esca pour les différencier d'autres troubles et maladies. Dans ce but, une comparaison entre approches SIFT et approches transfer learning récentes est alors conduite. Les résultats nous poussent alors à considérer une architecture deep learning simple (RetinaNet) pour la détection des symptômes sur les images, permettant d'estimer un niveau d'atteinte pour chaque pied.Le second capteur utilisé, une caméra hyperspectrale couvrant le spectre de 500 nm à 1300 nm, tente de répondre à une problématique plus expérimentale, à savoir le comportement spectral des pieds atteints par la maladie pouvant déboucher sur une détection précoce des pieds malades mais sans symptômes foliaires. Un protocole expérimental et une base de données de spectres sont alors constitués pour l'occasion. Les méthodes de réduction de la dimensionnalité permettent d'exploiter l'information hyperspectrale voire d'isoler les longueurs d'onde associées à chacune des deux classes. Les données ne permettent cependant pas, pour la plage de longueur d'onde mesurée et dans les conditions d'acquisition terrain, de réaliser une détection précoce de la maladie sur les pieds sans symptômes.Les différences et similarités entre chacune de ces deux applications, en terme de constitution de base de données, d'algorithmes, de difficultés et de potentiel d'application en conditions réelles sont discutées tout au long du manuscrit. / Grapevine wood diseases in the vineyard are responsible for significant economic losses in the wine industry. These diseases of fungal origin are caracterised by a degradation of the wooded part of the plant material and by the erratic appearance of characteristic symptoms on the leaf part. This thesis is dedicated to the study of these diseases (mainly esca disase) using two imaging sensors and proximal sensing.The issue of visible symptom detection is first addressed using an RGB color sensor to acquire an image for each plant automatically or semi-automatically. The recognition of symptoms is approached in two stages, firstly by considering the classification at leaf-scale and then the detection at the plant-scale. The particularity of this study is the inclusion of confounding factors in the classification problem, taking advantage of the shape information of esca symptoms to differentiate them from other disorders and diseases. For this purpose, a comparison between SIFT approaches and recent transfer learning approaches is then conducted. The results then lead us to consider a simple deep learning architecture (RetinaNet) for the detection of the symptoms on the images, making it possible to estimate a level of disease severity for each vineplant.The second sensor used, a hyperspectral camera covering the spectrum from 500 nm to 1300 nm, tries to tackle a more experimental problem, namely the spectral behavior of the diseased plants which may lead to early detection of diseased plants without foliar symptoms. An experimental protocol and a database of spectra are then formed for the occasion. The dimensionality reduction methods make it possible to exploit the hyperspectral information or even to isolate the wavelengths associated with each class. However, the data do not allow, for the measured wavelength range and in the field acquisition conditions, to perform early detection of the disease on the plant without symptoms.The differences and similarities between each of these two applications, in terms of database constitution, algorithms, difficulties and application potential in real conditions are discussed throughout the manuscript.

As well as the many benefits associated with the evolution of multispectral sensors into hyperspectral sensors there is also a considerable increase in storage space and the computational load to process the data. Consequently the remote sensing ommunity is investigating and developing statistical methods to alleviate these problems. / The research presented here investigates several approaches to reducing the dimensionality of hyperspectral remotely sensed data while maintaining the levels of accuracy achieved using the full dimensionality of the data. It was conducted with an emphasis on applications in maximum likelihood classification (MLC) of hyperspectral image data. An inherent characteristic of hyperspectral data is that adjacent bands are typically highly correlated and this results in a high level of redundancy in the data. The high correlations between adjacent bands can be exploited to realise significant reductions in the dimensionality of the data, for a negligible reduction in classification accuracy. / The high correlations between neighbouring bands is related to their response functions overlapping with each other by a large amount. The spectral band filter functions were modelled for the HyMap instrument that acquires hyperspectral data used in this study. The results were compared with measured filter function data from a similar, more recent HyMap instrument. The results indicated that on average HyMap spectral band filter functions exhibit overlaps with their neighbouring bands of approximately 60%. This is considerable and partly accounts for the high correlation between neighbouring spectral bands on hyperspectral instruments. / A hyperspectral HyMap image acquired over an agricultural region in the south west of Western Australia has been used for this research. The image is composed of 512 × 512 pixels, with each pixel having a spatial resolution of 3.5 m. The data was initially reduced from 128 spectral bands to 82 spectral bands by removing the highly overlapping spectral bands, those which exhibit high levels of noise and those bands located at strong atmospheric absorption wavelengths. The image was examined and found to contain 15 distinct spectral classes. Training data was selected for each of these classes and class spectral mean and covariance matrices were generated. / The discriminant function for MLC makes use of not only the measured pixel spectra but also the sample class covariance matrices. This thesis first examines reducing the parameterization of these covariance matrices for use by the MLC algorithm. The full dimensional spectra are still used for the classification but the number of parameters needed to describe the covariance information is significantly reduced. When a threshold of 0.04 was used in conjunction with the partial correlation matrices to identify low values in the inverse covariance matrices, the resulting classification accuracy was 96.42%. This was achieved using only 68% of the elements in the original covariance matrices. / Both wavelet techniques and cubic splines were investigated as a means of representing the measured pixel spectra with considerably fewer bands. Of the different mother wavelets used, it was found that the Daubechies-4 wavelet performed slightly better than the Haar and Daubechies-6 wavelets at generating accurate spectra with the least number of parameters. The wavelet techniques investigated produced more accurately modelled spectra compared with cubic splines with various knot selection approaches. A backward stepwise knot selection technique was identified to be more effective at approximating the spectra than using regularly spaced knots. A forward stepwise selection technique was investigated but was determined to be unsuited to this process. / All approaches were adapted to process an entire hyperspectral image and the subsequent images were classified using MLC. Wavelet approximation coefficients gave slightly better classification results than wavelet detail coefficients and the Haar wavelet proved to be a more superior wavelet for classification purposes. With 6 approximation coefficients, the Haar wavelet could be used to classify the data with an accuracy of 95.6%. For 11 approximation coefficients this figure increased to 96.1%. / First and second derivative spectra were also used in the classification of the image. The first and second derivatives were determined for each of the class spectral means and for each band the standard deviations were calculated of both the first and second derivatives. Bands were then ranked in order of decreasing standard deviation. Bands showing the highest standard deviations were identified and the derivatives were generated for the entire image at these wavelengths. The resulting first and second derivative images were then classified using MLC. Using 25 spectral bands classification accuracies of approximately 96% and 95% were achieved using the first and second derivative images respectively. These results are comparable with those from using wavelets although wavelets produced higher classification accuracies when fewer coefficients were used.

Au cours de cette thèse nous nous sommes intéressés à la problématique du démélange hyperspectral en milieux urbains. En particulier nous nous sommes penchés sur la prise en compte du phénomène de variabilité intra-classe dans les méthodes de démélange. La mise en évidence de la variabilité intra-classe a été le point de départ de cette étude. Nous avons ainsi constaté que ce phénomène était non-négligeable dans les milieux urbains et qu'il devait être pris en compte. En nous basant sur des modèles de mélange existants dans la littérature nous avons développé deux nouveaux modèles de mélange prenant en compte cette variabilité intra-classe. Le premier est un modèle de mélange linéaire. Le second est un modèle linéaire-quadratique qui permet de prendre en compte les réflexions multiples sur les bâtiments. Dans un premier temps nous ne nous sommes intéressés qu'au cas des modèles linéaires. Comme aucune méthode de la littérature ne permet d'effectuer le démélange à partir de nos modèles de mélange nous avons développé deux méthodes UP-NMF et IP-NMF. UP-NMF est une adaptation de la méthode NMF à notre modèle de mélange. Pour rendre compte de la notion de classes de matériaux purs une contrainte sur l'inertie des classes a été ajoutée à UP-NMF pour obtenir IP-NMF. Les premiers tests ont été effectués sur données semi-synthétiques et ont permis de déterminer l'impact de l'initialisation de ces méthodes sur leurs performances et de fixer le paramètre d'inertie. Les performances de UP-NMF et IP-NMF ont été comparées à celles des méthodes standards de démélange. Les seconds tests ont été effectués sur une portion d'image de Toulouse. Dans cette partie nous avons mis en évidence que, contrairement à des méthodes standards, les résultats de IP-NMF étaient peu sensibles à une erreur sur l'estimation du nombre de classes pures. Finalement nous avons développé une méthode de démélange linéaire-quadratique, LQIP-NMF, en nous basant sur le modèle que nous avons mis en place. Les tests de LQIP-NMF ont montré qu'en cas de trop forte variabilité intra-classe les effets de non-linéarité étaient de second ordre et qu'il ne semblait pas pertinent de les prendre en compte. / This work is devoted to unmixing for urban areas. We particularly focused on the impact of intra-class variability on unmixing. We first described the results of a study highlighting intra-class variability assessed in real images. It appeared that this phenomenon was significant and had to be included in the mixing models. Based on the state of the art we developed 2 new mixing models dealing with intra-class variability. The first one is a linear one. The second one is a linear-quadratic one which allows to consider multiple scattering effects on buildings. First only the linear mixing model was considered. Currently it does not exist any unmixing method able to deal with this new model. So two methods were developed, UP-NMF and IP-NMF. UP-NMF is a new unmixing method based on an extension of the standard NMF. To overcome UP-NMF limitations an extended method is proposed, IP-NMF, which limit the spreading of each class by adding an inertia constraint in the cost function. These methods were firstly tested on a semi-synthetic data set. These tests allowed us to study the impact of the initialisation on our methods performance and also to fix the inertia parameter. We also compared the results of UP-NMF and IP-NMF to the results obtained with standard methods. The second tests were performed on an image taken above Toulouse. It appeared that IP-NMF is less sensitive to an error in the estimation of classes number than standard methods. Finally we developed a linear-quadratic method, LQIP-NMF, dealing with the non-linear mixing model previously described. In cases of high intra-class variability, the quadratic terms are drowned in the large variability of materials. So it seems that it is not relevant to taking into account these non-linearities.

Les images hyperspectrales fournissent des informations intéressantes quant à la composition des sols, en particulier au niveau minéral, ce qui permet de caractériser efficacement les « groupements de minerai » et les « minerais isolés ». Ce travail de recherche avait pour objectif d'établir un système de cartographie 3D des minerais qui intègre des informations de surfaces (acquises par satellite ou à partir d'un aéronef) et des informations souterraines (par forages) à partir de données hyperspectrales. Le travail décrit dans ce mémoire s'articule de la manière suivante :- Pour la cartographie de surface, nous avons développé et optimisé des algorithmes pour mesurer de manière à la fois précise et homogène au niveau des transitions, le taux de minerais à toutes les longueurs d'ondes (de 0.2 à 12m) à partir de l'imagerie hyperspectrale par satellite et aéroportée. Un travail a été également fait à ce sujet pour améliorer la correction atmosphérique. Nous avons utilisé le modèle de BRDF de Hapke pour étudier la composition, linéaire et non linéaire, des modèles spectres- Pour la cartographie minéralogique souterraine, nous avons développé un système que nous avons appelé Field Imaging Spectrometer System (FISS), dont le verrou scientifique était dans le calcul et la mise en oeuvre de la précision sur les produits minéraux recherchés.- Nous avons mis cela en application par la suite, de manière à mieux comprendre le système minéral, et ce à partir de méthode utilisant les données hyperspectrales. Pour cela, nous avons utilisé les Systèmes d'Information Géographique (SIG), ce qui nous a permis de produire des simulations relatives à l'exploitation minière. / Hyperspectral remote sensing has been used successfully to identify and map abundances and compositional difference of mineral groups and single mineral phases. This research will toward developing a 3D mineral mapping system that integrate surface (airborne and satellite) and subsurface (drill core) hyperspectral remote sensing data and carries it into quantitative mineral systems analysis. The main content and result is introduced as follows:- For Surface mineralogy mapping, we have developed and optimized the processing methods for accurate, seamless mineral measurements using Airborne and Satellite hyperspectral image. This requires solutions for unmixing background effects from target minerals to leave residual scaled mineral abundances equivalent to vegetation-free pixels. Another science challenge is to improve the atmospheric correction. Also Hapke BRDF model is used on the study in the linear and nonlinear mineral spectral mixing models.- For the subsurface mineralogy mapping, we have developed Field Imaging Spectrometer System (FISS) and Drill Core Logging for the subsurface mineralogy mapping, the Key science challenges will be establishing the accuracy of derived mineral products through associated laboratory analysis, including investigations from SWIR into the thermal infrared for measuring minerals.- The 3D mineral maps derived from hyperspectral methods can distinctly improve our understanding of mineral system. We use GIS system integrating surface and subsurface mineralogy mapping, with 3D mineral models for demonstration exploitation of economic mineral deposits in test site.

Single-cell bio-analytical techniques play a pivotal role in contemporary biological and biomedical research. Among current high-throughput single-cell imaging methods, coherent Raman imaging offers both high bio-compatibility and high-throughput information-rich capabilities, offering insights into cellular composition, dynamics, and function. Coherent Raman imaging finds its value in diverse applications, ranging from live cell dynamic imaging, high-throughput drug screening, fast antimicrobial susceptibility testing, etc. In this thesis, I first present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-shot femtosecond stimulated Raman scattering (SRS) image. This method allows high-speed, high-throughput tracking of lipid droplet dynamics and drug response in live cells. Second, I provide image-based single-cell analysis in an engineered Escherichia coli (E. coli) population, confirming the chemical composition and subcellular structure organization of individual engineered E. coli cells. Additionally, I unveil metabolon formation in engineered E. coli by high-speed spectroscopic SRS and two-photon fluorescence imaging. Lastly, I present stimulated Raman-activated cell ejection (S-RACE) by integrating high-throughput SRS imaging, in situ image decomposition, and high-precision laser-induced cell ejection. I demonstrate the automatic imaging-identification-sorting workflow in S-RACE and advance its compatibility with versatile samples ranging from polymer particles, single live bacteria/fungus, and tissue sections. Collectively, these efforts demonstrate the valuable capability of SRS in high-throughput single-cell imaging and sorting, opening opportunities for a wide range of biological and biomedical applications.

<p>Nowadays, soybean is one the most consumed crops in the world. As the human population continuously increases, new phenotyping technology is needed to help plant scientists breed soybean that has high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most commonly used technologies for phenotyping. The current HSI techniques include HSI tower and remote sensing on an unmanned aerial vehicle (UAV) or satellite. There are several noise sources the current HSI technologies suffer from such as changes in lighting conditions, leaf angle, and other environmental factors. To reduce the noise on HS images, a new portable, leaf-level, high-resolution HSI device was developed for corn leaves in 2018 called LeafSpec. Due to the previous design requiring a sliding action along the leaf which could damage the leaf if used on a soybean leaf, a new design of the LeafSpec was built to meet the requirements of scanning soybean leaves. The new LeafSpec device protects the leaf between two sheets of glass, and the scanning action is automated by using motors and servos. After the HS images have been collected, the current modeling method for HS images starts by averaging all the plant pixels to one spectrum which causes a loss of information because of the non-uniformity of the leaf. When comparing the two modeling methods, one uses the mean normalized difference vegetation index (NDVI) and the other uses the NDVI heatmap of the entire leaf to predict the nitrogen content of soybean plants. The model that uses NDVI heatmap shows a significant increase in prediction accuracy with an R2 increase from 0.805 to 0.871. Therefore, it can be concluded that the changes occurring within the leaf can be used to train a better prediction model. </p> <p>Although the LeafSpec device can provide high-resolution leaf-level HS images to the researcher for the first time, it suffers from two major drawbacks: intensive labor needed to gather the image data and slow throughput. A new idea is proposed to use a UAV that carries a 6 degree of freedom (DOF) robotic arm with a LeafSpec device as an end-effect to automatically gather soybean leaf HS images. A new UAV is designed and built to carry the large payload weight of the robotic arm and LeafSpec.</p>