Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in the image. This hardware has not been studied in the context of autonomy as the sensors are large, expensive, and have non-trivial image capture times. This work presents three novel contributions: a Labeled Hyperspectral Image Dataset (LHID) of ground-level, outdoor objects based on typical scenes that a vehicle or pedestrian may encounter, an open-source hyperspectral interface software package (HSImage), and a feature selection based sensor design algorithm for object detection sensors (DLSD). These three contributions are novel and useful in the fields of hyperspectral data analysis, visual sensor design, and hyperspectral machine learning. The hyperspectral dataset and hyperspectral interface software were used in the design and testing of the sensor design algorithm. The LHID is shown to be useful for machine learning tasks through experimentation and provides a unique data source for hyperspectral machine learning. HSImage is shown to be useful for manipulating, labeling and interacting with hyperspectral data, and allows wavelength and classification based data retrieval, storage of labeling information and ambient light data. DLSD is shown to be useful for creating wavelength bands for a sensor design that increase the accuracy of classifiers trained on data from the LHID. DLSD shows accuracy near that of the full spectrum hyperspectral data, with a reduction in features on the order of 100 times. It compared favorably to other state-of-the-art wavelength feature selection techniques and exceeded the accuracy of an RGB sensor by 10%. / Ph. D. / To allow for better performance of autonomous vehicles in the complex road environment, identifying different objects in the roadway or near it is very important. Typically, cameras are used to identify objects and there has been much research into this task. However, the type of camera used is an RGB camera, the same used in consumer electronics, and it has a limited ability to identify colors. Instead, it only detects red, green, and blue and combines the results of these three measurements to simulate color. Hyperspectral cameras are specialized hardware that can detect individual colors, without having to simulate them. This study details an algorithm that will design a sensor for autonomous vehicle object identification that leverages the higher amount of information in a hyperspectral camera, but keep the simpler hardware of the RGB camera. This study presents three separate novel contributions: A database of hyperspectral images useful for tasks related to autonomous vehicles, a software tool that allows scientific study of hyperspectral images, and an algorithm that provides a sensor design that is useful for object identification. Experiments using the database show that it is useful for research tasks related to autonomous vehicles. The software tool is shown to be useful to interfacing between image files, algorithms and external software, and the sensor design algorithm is shown to be comparable to other such algorithms in accuracy, but outperforms the other algorithms in the size of the data required to complete the goal.

Tesis por compendio / [ES] El objetivo de esta tesis doctoral es evaluar la técnica de imagen hiperespectral en el rango visible e infrarrojo cercano, en combinación con técnicas quimiométricas para la evaluación de la calidad de la fruta en poscosecha de manera eficaz y sostenible. Con este fin, se presentan diferentes estudios en los que se evalúa la calidad de algunas frutas que por su valor económico, estratégico o social, son de especial importancia en la Comunidad Valenciana como son el caqui 'Rojo Brillante', la granada 'Mollar de Elche', el níspero 'Algerie' o diferentes cultivares de nectarina. En primer lugar se llevó a cabo la monitorización de la calidad poscosecha de nectarinas 'Big Top' y 'Magique' usando imagen hiperespectral en reflectancia y transmitancia. Al mismo tiempo se evaluó la transmitancia para la detección de huesos abiertos. Se llevó a cabo también un estudio para distinguir los cultivares 'Big Top' y "Diamond Ray", los cuales poseen un aspecto muy similar pero sabor diferente. En cuanto al caqui 'Rojo Brillante', la imagen hiperespectral fue estudiada por una parte para monitorear su madurez, y por otra parte para evaluar la astringencia de esta fruta, que debe ser completamente eliminada antes de su comercialización. Las propiedades físico-químicas de la granada 'Mollar de Elche' fueron evaluadas usando imagen de color e hiperespectral durante su madurez usando la información de la fruta intacta y de los arilos. Finalmente, esta técnica se usó para caracterizar e identificar los defectos internos y externos del níspero 'Algerie'. En la predicción de los índices de calidad IQI y RPI usando imagen en reflectancia y transmitancia se obtuvieron valores de R2 alrededor de 0,90 y en la discriminación por firmeza, una precisión entorno al 95 % usando longitudes de onda seleccionadas. En cuanto a la detección de huesos abiertos, el uso de la imagen hiperespectral en transmitancia obtuvo un 93,5 % de clasificación correcta de frutas con hueso normal y 100 % con hueso abierto usando modelos PLS-DA y 7 longitudes de onda. Los resultados obtenidos en la clasificación de los cultivares 'Big Top' y 'Diamond Ray' mostraron una fiabilidad superior al 96,0 % mediante el uso de modelos PLS-DA y 14 longitudes de onda seleccionadas, superando a la imagen de color (56,9 %) y a un panel entrenado (54,5 %). Con respecto al caqui, los resultados obtenidos indicaron que es posible distinguir entre tres estados de madurez con una precisión del 96,0 % usando modelos QDA y se predijo su firmeza obteniendo un valor de R2 de 0,80 usando PLS-R. En cuanto a la astringencia, se llevaron a cabo dos estudios similares en los que en el primero se discriminó la fruta de acuerdo al tiempo de tratamiento con altas concentraciones de CO2 con una precisión entorno al 95,0 % usando QDA. En el segundo se discriminó la fruta de acuerdo a un valor de contenido en taninos (0,04 %) y se determinó qué área de la fruta era mejor para realizar esta discriminación. Así se obtuvo una precisión del 86,9 % usando la zona media y 23 longitudes de onda. Los resultados obtenidos para la granada indicaron que la imagen de color e hiperespectral poseen una precisión similar en la predicción de las propiedades fisicoquímicas usando PLS-R y la información de la fruta intacta. Sin embargo, cuando se usó la información de los arilos, la imagen hiperespectral fue más precisa. En cuanto a la discriminación del estado de madurez usando PLS-DA, la imagen hiperespectral ofreció mayor precisión, 95,0 %, usando la información de la fruta intacta y del 100 % usando la de los arilos. Finalmente, los resultados obtenidos para el níspero indicaron que la imagen hiperespectral junto con el método de clasificación XGBOOST pudo discriminar entre muestras con y sin defectos con una precisión del 97,5 % y entre muestras sin defectos o con defectos internos o externos con una precisión del 96,7 %. Además fue posible distinguir entre los dife / [CA] L'objectiu de la present tesi doctoral se centra en avaluar la capacitat de la imatge hiperespectral en el rang visible i infraroig pròxim, en combinació amb mètodes quimiomètrics, per a l'avaluació de la qualitat de la fruita en post collita de manera eficaç i sostenible. A aquest efecte, es presenten diferents estudis en els quals s'avalua la qualitat d'algunes fruites que pel seu valor econòmic, estratègic o social, són d'especial importància a la Comunitat Valenciana com són el caqui 'Rojo Brillante', la magrana 'Mollar de Elche', el nispro 'Algerie' o diferents cultivares de nectarina. En primer lloc es va dur a terme la monitorització de la qualitat post collita de nectarines 'Big Top' i 'Magique' per mitjà d'imatge hiperespectral en reflectància i trasnmitancia. Així mateix es va avaluar la transmitància per a la detecció d'ossos oberts. Es va dur a terme també un estudi per distingir els cultivares 'Big Top' i 'Diamond Ray', els quals posseeixen un aspecte molt semblant però sabor diferent. Pel que fa al caqui 'Rojo Brillante', la imatge hiperespectral va ser estudiada d'una banda per a monitoritzar la seua maduresa, i per un altre costat per avaluar l'astringència, que ha de ser completament eliminada abans de la seua comercialització. Les propietats fisicoquímiques de la magrana 'Mollar de Elche' van ser avaluades per la imatge de color i hiperespectral durant la seua maduresa usant la informació de la fruita intacta i els arils. Finalment, aquesta tècnica es va fer servir per caracteritzar i identificar els defectes interns i externs del nispro 'Algerie'. En la predicció dels índexs de qualitat IQI i RPI usant imatge en reflectància com en trasnmitancia es van obtindre valors de R2 al voltant de 0,90 i en la discriminació per fermesa una precisió entorn del 95,0 % utilitzant longituds d'ona seleccionades. Pel que fa a la detecció d'ossos oberts, l'ús de la imatge hiperespectral en transmitància va obtindre un 93,5 % classificació correcta de fruites amb os normal i 100 % amb os obert usant models PLS-DA i 7 longituds d'ona. Els resultats obtinguts en la classificació dels cultivares 'Big Top' i 'Diamond Ray' van mostrar una fiabilitat superior al 96,0 % per mitjà de l'ús de models PLS-DA i 14 longituds d'ona, superant a la imatge de color (56,9 %) i a un panell sensorial entrenat (54,5 %). Quant al caqui, els resultats obtinguts van indicar que és possible distingir entre tres estats de maduresa amb una precisió del 96,0 % usant models QDA i es va predir la seua fermesa obtenint un valor de R2 de 0,80 usant PLS-R. Pel que fa a l'astringència, es van dur a terme dos estudis similars en què el primer es va discriminar la fruita d'acord al temps de tractament amb altes concentracions de CO2 amb una precisió al voltant del 95,0 % usant QDA. En el segon, es va discriminar la fruita d'acord a un valor de contingut en tanins (0,04 %) i es va determinar quina part de la fruita era millor per a realitzar aquesta discriminació. Així es va obtindre una precisió del 86,9 % usant la zona mitjana i 23 longituds d'ona. Els resultats obtinguts per la magrana van indicar que la imatge de color i hiperespectral posseïxen una precisió semblant a la predicció de les propietats fisicoquímiques usant PLS-R i la informació de la fruita intacta. No obstant això, quan es va usar la informació dels arils, la imatge hiperespectral va ser més precisa. Quant a la discriminació de l'estat de maduresa usant PLS-DA, la imatge hiperespectral va oferir major precisió (95,0 %) usant la informació de la fruita intacta i del 100 % usant la dels arils. Finalment, els resultats obtinguts pel nispro indiquen que la imatge hiperespectral juntament amb el mètode de classificació XGBOOST va poder discriminar entre mostres amb i sense defectes amb una precisió del 97,5 % i entre mostres sense defectes o amb defectes interns o externs amb una precisió del 96,7 %. A més, va ser possible distingir entre / [EN] The objective of this doctoral thesis is to evaluate the potential of the hyperspectral imaging in the visible and near infrared range in combination with chemometrics for the assessment of the postharvest quality of fruit in a non-destructive, efficient and sustainable manner. To this end, different studies are presented in which the quality of some fruits is evaluated. Due to their economic, strategic or social value, the selected fruits are of special importance in the Valencian Community, such as Persimmon 'Rojo Brillante', the pomegranate 'Mollar de Elche', the loquat 'Algerie' or different nectarine cultivars. First, the quality monitoring of 'Big Top' and 'Magique' nectarines was carried out using reflectance and transmittance images. At the same time, transmittance was evaluated for the detection of split pit. In addition, a classification was performed to distinguish the 'Big Top' and 'Diamond Ray' cultivars, which look very similar but have different flavour. Whereas that for the 'Rojo Brillante' persimmon, the hyperspectral imaging was studied on the one hand to monitor its maturity, and on the other hand to evaluate the astringency of this fruit, which must be completely eliminated before its commercialization. The physicochemical properties of the 'Mollar de Elche' pomegranate were evaluated by means of hyperspectral and colour imaging during its maturity using the information from the intact fruit and arils. Finally, this technique was used to characterise and identify the internal and external defects of the 'Algerie' loquat. In the prediction of the IQI and RPI quality indexes using reflectance and transmittance images, R2 values around 0.90 were obtained and in the discrimination according to firmness, accuracy around 95.0 % using selected wavelengths was obtained. Regarding the split pit detection, the use of the hyperspectral image in transmittance mode obtained a 93.5 % of fruits with normal bone correctly classified and 100% with split pit using PLS-DA models and 7 wavelengths. The results obtained in the classification of 'Big Top' and 'Diamond Ray' fruits show accuracy higher than 96.0 % by using PLS-DA models and 14 selected wavelengths, higher than the obtained with colour image (56.9 %) and a trained panel (54.5 %). According to persimmon, the results obtained indicated that it is possible to distinguish between three states of maturity with an accuracy of 96.0 % using QDA models and its firmness was predicted obtaining a R2 value of 0.80 using PLS-R. Regarding astringency, two similar studies were carried out. In the first study, the fruit was classified according to the time of treatment with high concentrations of CO2 with a precision of around 95.0 % using QDA. In the second, the fruit was discriminated according to a threshold value of soluble tannins (0.04 %) and was determined what fruit area was better to perform this discrimination. Thus, an accuracy of 86.9 % was obtained using the middle area and 23 wavelengths. The results obtained for the pomegranate indicated that the use of colour and hyperspectral images have a similar precision in the prediction of physicochemical properties using PLS-R and the intact fruit information. However, when the information from the arils was used, the hyperspectral image was more accurate. Regarding the discrimination by the state of maturity using PLS-DA, the hyperspectral image offered greater precision, of 95.0 % using the information from the intact fruit and 100 % using that from the arils. Finally, the results obtained for the 'Algerie' loquat indicated that the hyperspectral image with the XGBOOST classification method could discriminate between sound samples and samples with defects with accuracy of 97.5 % and between sound samples or samples with internal or external defects with an accuracy of 96.7 %. It was also possible to distinguish between the different defects with an accuracy of 95.9 %. / Munera Picazo, SM. (2019). Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/125954 / Compendio

<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>

Doctor of Philosophy / Department of Food Science / Randall K. Phebus / Intervention techniques to control foodborne pathogens, and rapid identification of pathogens in food are of vital importance to ensure food safety. Therefore, the first objective of this research was to study the efficacy of radio frequency dielectric heating (RFDH) against C. sakazakii and Salmonella spp. in nonfat dry milk (NDM) at 75, 80, 85, or 90°C. Using thermal-death-time (TDT) disks, D-values of C. sakazakii in high heat (HH)- and low heat (LH)-NDM were 24.86 and 23.0 min at 75°C, 13.75 and 7.52 min at 80°C, 8.0 and 6.03 min at 85°C, and 5.57 and 5.37 min at 90°C, respectively. D-values of Salmonella spp. in HH- and LH-NDM were 23.02 and 24.94 min at 75°C, 10.45 and 12.54 min at 80°C, 8.63 and 8.68 min at 85°C, and 5.82 and 4.55 min at 90°C, respectively. The predicted (TDT) and observed (RFDH) destruction of C. sakazakii and Salmonella spp. were in agreement, indicating that the organisms' behavior was similar regardless of the heating system (conventional vs. RFDH). However, RFDH can be used as a faster and more uniform heating method for NDM to achieve the target temperatures. The second objective of this research was to study if hyperspectral imaging can be used for the rapid identification and differentiation of various foodborne pathogens. Four strains of C. sakazakii, 5 strains of Salmonella spp., 8 strains of E. coli, and 1 strain each of L. monocytogenes and S. aureus were used in the study. Principal component analysis and kNN (k-nearest neighbor) were used to develop classification models, which were then validated using a cross-validation technique. Classification accuracy of various strains within genera including C. sakazakii, Salmonella spp. and E. coli, respectively was 100%; except within C. sakazakii, strain BAA-894, and within E. coli, strains O26, O45 and O121 had 66.67% accuracy. When all strains were studied together (irrespective of their genera) for the classification, only C. sakazakii P1, E. coli O104, O111 and O145, S. Montevideo, and L. monocytogenes had 100% classification accuracy; whereas, E. coli O45 and S. Tennessee were not classified (classification accuracy of 0%).

Le phénotypage consiste à caractériser les plantes et leur comportement en vue de la sélection génétique. Cette étude a évalué le potentiel de l'imagerie hyperspectrale de proximité pour répondre à ces besoins. Elle s'appuie sur le lien existant entre la physiologie des plantes et leurs propriétés optiques. Cette étude a montré qu'il est possible de retrouver la réflectance des feuilles en dépit d'un éclairage naturel variable. La procédure de correction mise en place permet de retrouver la réflectance vraie de feuilles à plat et introduit un effet additif (dû à la réflexion spéculaire), un effet multiplicatif (dû au niveau d'éclairement) et un effet non linéaire (dû aux réflexions multiples) sur les feuilles inclinées des plantes au champ. Cependant, nous avons montré également que, grâce à des pré-traitements des spectres adéquats et à la PLS (Partial Least Square regression), la concentration en azote est accessible à partir de la réflectance (400-1000~nm) de feuilles fraîches sur pied. L'étude de spectres simulés a montré que la non prise en compte des réflexions multiples dans l'étalonnage d'un modèle conduisait à une surestimation de la concentration en azote des feuilles subissant des réflexions multiples. Enfin, cette étude a illustré l'intérêt de l'imagerie hyperspectrale de proximité par rapport à la spectrométrie ponctuelle. Le fait d'avoir une image, combiné à la haute résolution spatiale permet d'obtenir des données plus représentatives de la parcelle et de calculer une vitesse de fermeture de couvert. La réalisation de cartographies d'azote permet de suivre la concentration en azote dans différents étages foliaires ou parties d'une même feuille. / Henotyping consists in characterising plants and their behavior with the aim of the genetic selection. This study estimated the potential of the close-range hyperspectral imaging to meet these needs. It leans on the link existing between plant physiology and their optical properties. This study showed that it is possible to find leaf reflectance in spite of a variable natural lighting. The developed correction procedure allows finding the true reflectance of flat leaves and introduces an additive effect (due to specular reflection), a multiplicative effect (due to illumination level) and a not linear effect (due to the multiple reflections) on inclinated leaves of plants in the field. However, we also showed that, thanks to adequate preprocessing of the spectra and to PLS (Partial Least Square regression), the nitrogen concentration is accessible from the reflectance (400-1000~nm) of fresh leaves on standing plants. The study of simulated spectra showed that the not consideration of the multiple reflections in the calibration of a model lead to an overestimation of the nitrogen concentration leaves undergoing multiple reflections. Finally, this study illustrated the interest of close-range hyperspectral imaging with regard to the punctual spectrometry. The fact of having an image, combined with the high spatial resolution allows to obtain more representative data of the plot and to calculate a speed of cover closure. Nitrogen mappings allow following the nitrogen concentration in various leaf level or parts of the same leaf.

Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals. The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy. The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels. / May 2016

L’imagerie hyperspectrale est maintenant très développée dans les applications de télédétection. Il y a principalement deux manières de construire les imageurs associés : la première méthode utilise un réseau et une fente, et l’image spectrale est acquise ligne par la ligne le long de la trajectoire du porteur. La seconde est basée sur le principe de la spectrométrie par transformée de Fourier (TF). Certains des systèmes utilisés sont construits de manière à enregistrer l’interférogramme de chaque point de la scène suivant le déplacement dans le champ. Le spectre de la lumière venant d’un point de la scène est alors calculé par la transformée de Fourier de son interférogramme. Les imageurs classiques basés sur des réseaux sont plus simples à réaliser et les données qu’ils fournissent sont souvent plus faciles à interpréter. Cependant, les spectro-imageurs par TF fournissent un meilleur rapport signal sur bruit si la source principale de bruit vient du détecteur.Dans la première partie de cette thèse, nous étudions l’influence de différents types de bruit sur les architectures classiques et TF afin d’identifier les conditions dans lesquelles ces dernières présentent un avantage. Nous étudions en particulier l’influence des bruits de détecteur, de photons, des fluctuations de gain et d’offset du détecteur et des propriétés de corrélation spatiale des fluctuations d’intensité du spectre mesuré. Dans la seconde partie, nous présentions la conception, la réalisation et les premiers résultats d’un imageur basé sur un interféromètre de Michelson à dièdres statique nommé DéSIIR (Démonstrateur de Spectro-Imagerie Infrarouge). Les premiers résultats montrent, qu’en mode spectromètre simultané, DéSIIR permet la restitution du spectre avec les spécifications requises dans le cadre des applications recherchées, c'est-à-dire détecter avec une résolution d environ 25 cm-1 un object de quelques degrés plus chaud que le fond de la scène et présentant une signature spectrale entre 3 et 5 juin. En mode spectromètre imageur, après recalage des images, il est possible de reconstruire le spectre de chaque point de la scène observée. / Hyperspectral imaging is now very important in remote sensing applications. There are two main ways to build such imagers : the first one uses a grating and a slit, and the spectral image is acquired line by line along the track of the carrier. The second way is to use the principle of Fourier transform (FT) spectrometry. Some of these systems are built in such a way that they record the interferogram of each point of the scene as it moves through the field of view. The spectrum of the light coming from a particular point is then calculated by the Fourier transform of its interferogram. Classical gratting-based spectral imagers are easier to build and the data they provide a better signal to noise ratio if the main source of noise comes from the detector.In the first part of this thesis, we study the influence of various types of noise on the classic and TF-based architectures to identify the conditions in which these last ones present an advantage. We study particularly the influence of detector noise, photons noise, detector gain and offset fluctuations and spatial correlation properties of the intensity fluctuations. In the second part, we present the conception, the realization and the first results of an imager bases on a Michelson interferometer with dihedrons named DéSIIR (“Démonstrateur de Spectro Imagerie Infrarouge”). The first results show that, in simultaneous spectrometer mode, DéSIIR allows the reconstruction of the spectrum with respect to the specific requirements, which are to be able to detect an objet of some degrees warmer than the background of the scene observed with a resolution of about 25 cm -1. In imager mode, this reconstruction is performed for each point of the scene.

Cette thèse contribue à la recherche de méthodes de détection de signaux inconnus à très faible Rapport Signal-à-Bruit. Ce travail se concentre sur la définition, l’étude et la mise en œuvre de méthodes efficaces capables de discerner entre observations caractérisées seulement par du bruit de celles qui au contraire contiennent l’information d’intérêt supposée parcimonieuse. Dans la partie applicative, la pertinence de ces méthodes est évaluée sur des données hyperspectrales. Dans la première partie de ce travail, les principes à la base des tests statistiques d’hypothèses et un aperçu général sur les représentations parcimonieuses, l’estimation et la détection sont introduits. Dans la deuxième partie du manuscrit deux tests d’hypothèses statistiques sont proposés et étudiés, adaptés à la détection de signaux parcimonieux. Les performances de détection des tests sont comparés à celles de méthodes fréquentistes et Bayésiennes classiques. Conformément aux données tridimensionnelles considérées dans la partie applicative, et pour se rapprocher de scénarios plus réalistes impliquant des systèmes d’acquisition de données, les méthodes de détection proposées sont adaptées de façon à exploiter un modèle plus précis basé sur des dictionnaires qui prennent en compte l’effet d’étalement spatio-spectral de l’information causée par les fonctions d’étalement du point de l’instrument. Les tests sont finalement appliqués à des données astrophysiques massives de type hyperspectral dans le contexte du Multi Unit Spectroscopic Explorer de l’Observatoire Européen Austral. / This thesis deals with the problem of detecting unknown signals at low Signal- to- Noise Ratio. This work focuses on the definition, study and implementation of efficient methods able to discern only-noise observations from those that presumably carry the information of interest in a sparse way. The relevance of these methods is assessed on hyperspectral data as an applicative part. In the first part of this work, the basic principles of statistical hypothesis testing together with a general overview on sparse representations, estimation and detection are introduced. In the second part of the manuscript, two statistical hypotheses tests are proposed and studied. Both are adapted to the detection of sparse signals. The behaviors and the relative differences between the tests are theoretically investigated through a detailed study of their analytical and structural characteristics. The tests’ detection performances are compared with those of classical frequentist and Bayesian methods. According to the three-dimensional data sets considered in the applicative part, and to be closer to realistic scenarios involving data acquisition systems, the proposed detection strategies are then adapted in order to: i) account for spectrally variable noise; ii) exploit the spectral similarities of neighbors pixels in the spatial domain and iii) exploit the greater accuracy brought by dictionary-based models, which take into account the spatiospectral blur of information caused by instrumental Point Spread Functions. The tests are finally applied to massive astrophysical hyperspectral data in the context of the European Southern Observatory’s Multi Unit Spectroscopic Explorer.

Seizures containing large numbers of units of narcotics, goods dangerous to health and doping are often sent to the Swedish National Forensic Centre (NFC). Only a fraction of these capsules or tablets can be analyzed, therefore the samples need to represent the whole seizure. If the samples show content variations, Near Infrared (NIR) spectroscopy in combination with hyperspectral imaging has been shown to be a promising tool to gauge the homogeneity in the seizures based on chemical content. The objective of this thesis was to further develop and then validate a method for the separation of pregabalin and gabapentin capsules using NIR hyperspectral imaging and Principal Component Analysis (PCA). Capsules containing different amounts of pregabalin and gabapentin were prepared and analyzed. Additionally, authentic seizures were analyzed to confirm that the method fulfilled its purpose. The result of this study showed that use of hyperspectral data in the wavelength range 1650-1750 nm gave the best differentiation between pregabalin and gabapentin capsules. Capsules containing the ratio 70-30 % gabapentin and pregabalin could be separated distinctively from capsules containing pure gabapentin. Multiple authentic seizures could be separated into groups correctly depending on the capsules or tablets content.

<p>This dissertation explores computational optical imaging methods to circumvent the physical limitations of classical sensing. An ideal imaging system would maximize resolution in time, spectral bandwidth, three-dimensional object space, and polarization. Practically, increasing any one parameter will correspondingly decrease the others.</p><p>Spectrometers strive to measure the power spectral density of the object scene. Traditional pushbroom spectral imagers acquire high resolution spectral and spatial resolution at the expense of acquisition time. Multiplexed spectral imagers acquire spectral and spatial information at each instant of time. Using a coded aperture and dispersive element, the coded aperture snapshot spectral imagers (CASSI) here described leverage correlations between voxels in the spatial-spectral data cube to compressively sample the power spectral density with minimal loss in spatial-spectral resolution while maintaining high temporal resolution.</p><p>Photography is limited by similar physical constraints. Low f/# systems are required for high spatial resolution to circumvent diffraction limits and allow for more photon transfer to the film plain, but require larger optical volumes and more optical elements. Wide field systems similarly suffer from increasing complexity and optical volume. Incorporating a multi-scale optical system, the f/#, resolving power, optical volume and wide field of view become much less coupled. This system uses a single objective lens that images onto a curved spherical focal plane which is relayed by small micro-optics to discrete focal planes. Using this design methodology allows for gigapixel designs at low f/# that are only a few pounds and smaller than a one-foot hemisphere.</p><p>Computational imaging systems add the necessary step of forward modeling and calibration. Since the mapping from object space to image space is no longer directly readable, post-processing is required to display the required data. The CASSI system uses an undersampled measurement matrix that requires inversion while the multi-scale camera requires image stitching and compositing methods for billions of pixels in the image. Calibration methods and a testbed are demonstrated that were developed specifically for these computational imaging systems.</p> / Dissertation