Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken. This thesis analyzes three key aspects of HSI exploitation: statistical data modeling, covariance estimation from training data, and dimension reduction. These items are part of standard processing schemes, and it is worthwhile to understand and quantify the impact that various assumptions for these items have on target detectability and detection statistics. First, the accuracy and applicability of the standard Gaussian (i.e., Normal) model is evaluated, and it is shown that the elliptically contoured t-distribution (EC-t) sometimes offers a better statistical model for HSI data. A finite mixture approach for EC-t is developed in which all parameters are estimated simultaneously without a priori information. Then the effects of making a poor covariance estimate are shown by including target samples in the training data. Multiple test cases with ground targets are explored. They show that the magnitude of the deleterious effect of covariance contamination on detection statistics depends on algorithm type and target signal characteristics. Next, the two most widely used dimension reduction approaches are tested. It is demonstrated that, in many cases, significant dimension reduction can be achieved with only a minor loss in detection performance. In addition, a concise development of key HSI detection algorithms is presented, and the state-of-the-art in adaptive detectors is benchmarked for land mine targets. Methods for detection and identification of airborne gases using hyperspectral imagery are discussed, and this application is highlighted as an excellent opportunity for future work.

In this thesis, 2D Local Discriminant Bases (LDB) algorithm is used to 2D search structure to classify remotely sensed data. 2D Linear Discriminant Analysis (LDA) method is converted into an M-ary classifier by combining majority voting principle and linear distance parameters. The feature extraction algorithm extracts the relevant features by removing the irrelevant ones and/or combining the ones which do not represent supplemental information on their own. The algorithm is implemented on a remotely sensed airborne data set from Tippecanoe County, Indiana to evaluate its performance. The spectral and spatial-frequency features are extracted from the multispectral data and used for classifying vegetative species like corn, soybeans, red clover, wheat and oat in the data set.

Tissue optical properties, absorption, scattering and fluorescence, reveal important information about health, and holds the potential for non-invasive diagnosis and therefore earlier treatment for many diseases. On the other hand, tissue structure determines its function. Studying tissue structural properties helps us better understand structure-function relationship. Optical imaging is an ideal tool to study these tissue properties. However, conventional optical imaging techniques have limitations, such as not being able to quantitatively evaluate tissue absorption and scattering properties and only providing volumetrically averaged quantities with no depth control capability. To better study tissue properties, we integrated spatial frequency domain imaging (SFDI) with conventional reflectance imaging modalities. SFDI is a non-invasive, non-contact wide-field imaging technique which utilizes structured illumination to probe tissues. SFDI imaging is able to accurately quantify tissue optical properties. By adjusting spatial frequency, the imaging depth can be tuned which allows for depth controlled imaging. Especially at high spatial frequency, SFDI reflectance image is more sensitive to tissue scattering property than absorption property. The imaging capability of SFDI allows for studying tissue properties from a whole new perspective. In our study, we developed both benchtop and handheld SFDI imaging systems to accommodate different applications. By evaluating tissue optical properties, we corrected attenuation in fluorescence imaging using an analytical model; and we quantified optical and physical properties of skin diseases. By imaging at high spatial frequency, we demonstrated that absorption in fluorescence imaging can also be reduced because of a reduced imaging depth. This correction can be performed in real-time at 19 frames/second. Furthermore, fibrous structures orientation from the superficial layer can be accurately quantified in a multi-layered sample by limiting imaging depth. Finally, we color rendered SFDI reflectance image at high spatial frequency to reveal structural changes in skin lesions.

Cereal grains are an important part of human diet; hence, there is a need to maintain high quality and these grains must be free of physical and biological contaminants. A procedure was developed to differentiate physical contaminants from wheat using NIR (1000-1600 nm) hyperspectral imaging. Three experiments were conducted to select the best combinations of spectral pre-processing technique and statistical classifier to classify physical contaminants: seven foreign material types (barley, canola, maize, flaxseed, oats, rye, and soybean); six dockage types (broken wheat kernels, buckwheat, chaff, wheat spikelets, stones, and wild oats); and two animal excreta types (deer and rabbit droppings) from Canada Western Red Spring (CWRS) wheat. These spectra were processed using five spectral pre-processing techniques (first derivative, second derivative, Savitzky-Golay (SG) smoothing and differentiation, multiplicative scatter correction (MSC), and standard normal variate (SNV)). The raw and pre-processed data were classified using Support Vector Machines (SVM), Naïve Bayes (NB), and k-nearest neighbors (k-NN) classifiers. In each experiment, two-way and multi-way classifications were conducted. Among all the contaminant types, stones, chaff, deer droppings and rabbit droppings were classified with 100% accuracy using the raw reflectance spectra and different statistical classifiers. The SNV technique with k-NN classifier gave the highest accuracy for the classification of foreign material types from wheat (98.3±0.2%) and dockage types from wheat (98.9±0.2%). The MSC and SNV techniques with SVM or k-NN classifier gave perfect classification (100.0±0.0%) for the classification of animal excreta types from wheat. Hence, the SNV technique with k-NN classifier was selected as the best model. Two separate model performance evaluation experiments were conducted to identify and quantify (by number) the amount of contaminant type present in wheat. The overall identification accuracy of the first degree of contamination (one contaminant type with wheat) and the highest degree of contamination (all the contaminant type with wheat) was 97.6±1.6% and 92.5±6.5%, for foreign material types; 98.0±1.8% and 94.3±6.2%r for dockage types; and 100.0±0.0% and 100.0±0.0%, respectively for animal excreta types. The canola, stones, deer, and rabbit droppings were perfectly quantified (100.0±0.0%) at all the levels of contaminations. / February 2016

The aim of this thesis is to study and evaluate the application of hyperspectral image analysis as a non-destructive analysis method for historical mortars. This method was applied on 35 sampled mortars in varying sizes and type from the inner walls of the fortified medieval city Carcassonne. By using near infrared spectroscopy and classifying the complex multivariate data by applying the SIMCA method (Soft Independent Modelling of Class Analogies) it is possible to conduct an in depth analysis of the samples. This can then further our understanding about the construction phases as well as construction techniques used as indicated through the chemometric analysis that can identify and group the mortars in accordance to raw material and transformation process. From this could four distinct groups be found in the PCA models, two Roman periods and two high medieval periods, allowing to study Carcassonne prior to and after its enclosure. A find from the first Roman period indicates on a bathhouse or public building existing prior to the construction of the defensive wall, leading to the hypothesis that maybe more parts of the inner wall might contain older structures like this. The application of hyperspectral image analysis on historical mortars has proven itself a useful tool and simple method for studying mortars. / Målet med denna uppsats var att studera och evaluera applikationen av hyperspektral bildanalys som en icke-destruktiv analysmetod på historiskt murbruk. Instrumentet applicerades på 35 murbruksprover i varierande storlek och typ tagna från de inre murarna av den befästa medeltida staden Carcassonne. Med nära infraröd spektroskopi och klassificering av den multivariata genom SIMCA metoden (Soft Independent Modelling of Class Analogies) var det möjligt att göra en djupgående analys av proverna. Detta tillvägagångssätt kan då främja vår förståelse om stadens konstruktionsfaser och konstruktionstekniker som indikeras genom den chemometriska analysen som kan identifiera murbruket utefter råmaterial samt hur murbruket tillverkats. Från dessa metoder kunde fyra distinkta grupper finnas i PCA modellerna, två romerska perioder och två högmedeltida perioder, vilket öppnade för tolkning både innan och efter stadsmurarna rests. Ett fynd från den första romerska perioden indikerar på förekomsten av ett badhus eller publik byggnad vars väggar sedan återanvänts vid konstruktionen av den inre stadsmuren. Detta fynd leder till hypotesen att potentiellt andra delar av den inre stadsmuren kan innehålla väggar från äldre byggnader som denna. Applikationen av hyperspektral bildanalys på historiskt murbruk har påvisat sig ett användbart verktyg och simpel metod för att studera murbruk.

Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (NHMC models) to characterize wavelet coefficients which capture the spectrum structural information at multiple levels. Experimental results show that the approach based on NHMC models outperforms existing approaches relevant in classification tasks.

The selection of a representative sample is a delicate problem when drug seizures comprised of large number of units arrive at the Swedish National Forensic Centre (NFC). If deviating objects in the selected sample size are found, additional analyzes are required to investigate how representative the results are for the entire population. This generates further pressure on operational analysis flow. With the goal to provide a tool which forensic scientists at NFC can base their assessment of the representative nature of the selected sampling of large drug seizures on, this project investigated the possibilities of evaluating the level of homogeneity in drug seizures using near-infrared (NIR) hyperspectral imaging along with principal component analysis (PCA). A total of 27 sample groups (homogeneous, heterogeneous and seized sample groups) were analyzed and different predictive models were developed. The models were either based on quantifying the variation in NIR spectra or in PCA scores plots. It was shown that in the spectral range of 1300-2000 nm, using a pre-processing combination of area normalization, quadratic (second polynomial) detrending and mean centering, promising predictive abilities of the models in their evaluation of the level of homogeneity in drug seizures were achieved. A model where the approximated signal-dependent variation was related to the quotient of significant and noise explained variance given by PCA indicated most promising predictive abilities when quantifying the variation in NIR spectra. Similarly, a model where a rectangular area, defined by the maximum distances along PC1 and PC2, was related to the cumulative explained variance of the two PCs showed most promising predictive abilities when quantifying the variation in PCA scores plots. Different zones for which within sample groups are expected to appear based upon their degree of homogeneity could be established for both models. The two models differed in sensitivity. However, more comprehensive studies are required to evaluate the models applicability from an operational point-of-view.

Abstract Purpose: This study evaluated the use of hyperspectral imaging for the determination of the resection margin during colorectal resections instead of clinical macroscopic assessment. Methods: The used hyperspectral camera is able to record light spectra from 500 to 1000 nm and provides information about physiologic parameters of the recorded tissue area intraoperatively (e.g., tissue oxygenation and perfusion). We performed an open-label, single-arm, and non-randomized intervention clinical trial to compare clinical assessment and hyperspectral measurement to define the resection margin in 24 patients before and after separation of the marginal artery over 15 min; HSI was performed each minute to assess the parameters mentioned above. Results: The false color images calculated from the hyperspectral data visualized the margin of perfusion in 20 out of 24 patients precisely. In the other four patients, the perfusion difference could be displayed with additional evaluation software. In all cases, there was a deviation between the transection line planed by the surgeon and the border line visualized by HSI (median 1 mm; range - 13 to 13 mm). Tissue perfusion dropped up to 12% within the first 10 mm distal to the border line. Therefore, the resection area was corrected proximally in five cases due to HSI record. The biggest drop in perfusion took place in less than 2 min after devascularization. Conclusion: Determination of the resection margin by HSI provides the surgeon with an objective decision aid for assessment of the best possible perfusion and ideal anastomotic area in colorectal surgery.:Inhaltsverzeichnis Inhaltsverzeichnis................................................................. I 1 Einführung............................................................................. 1 1.1 Anastomoseninsuffizienz...................................................1 1.2 Methodik Hyperspectral Imaging (HSI)............................. 3 1.3 Einsatzbereiche der Hyperspektral-Kamera..................... 5 1.4 Chirurgische Technik........................................................ 6 1.5 Studienplanung................................................................. 7 1.6 Vergleich der HSI-Technik mit weiteren Messmethoden...8 2 Publikation...............................................................................11 3 Zusammenfassung der Arbeit............................................... 21 4 Literaturverzeichnis............................................................... 26 5 Anhang.................................................................................... 30 Darstellung des eigenen Beitrags.........................................34 Eigenständigkeitserklärung...................................................35 Lebenslauf.............................................................................. 36 Danksagung........................................................................... 38

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20054号 / 医博第4162号 / 新制||医||1018(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 湊谷 謙司, 教授 富樫 かおり, 教授 木村 剛 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

<div>Increasing the capability of maize hybrids to use nitrogen (N) more efficiently is a common goal that contributes to reducing farmer costs and limiting negative environmental impacts. However, development of such hybrids is costly and arduous due to the repeated need for laborious field and laboratory measurements of whole-plant biomass and N uptake in large early-stage breeding programs. This research evaluated alternative in-season methodologies, including field-based physiological measurements and hyperspectral remote imagery, as surrogate or predictive measures of important end-of-season N efficiency parameters. </div><div><br></div><div>Differences in the genetic potential of 285 hybrids (derived from test crosses to a single tester) with respect to N Internal Efficiency (NIE, grain yield per unit of accumulated plant N) were investigated at two Indiana locations in 2015. The hybrids (representing both early and late maturity groups) were grown at one low N rate and a single plant density. Germplasm sources included USDA, Dow AgroSciences, and “control” checks. Various secondary traits (plant height, stalk diameter, LAI, green leaf counts, and SPAD measurements) were evaluated for their potential role as surrogate measurements for N metrics at maturity (R6) such as plant N content or NIE. Four band (RGB, NIR) multispectral airborne remote sensing imagery at R1 and R3/R4 was also collected. The key findings were: 1) identification of the 10 highest and 10 lowest ranked hybrids for each maturity group in both grain yield and NIE categories, 2) the discovery of 5 top performing hybrids which had both high NIE and high yield, 3) strong correlations of leaf SPAD (at R1 and R2/R3) to grain yield or plant N at R6, 4) none of the surrogate measurements were significantly correlated to NIE, and 5) vegetation indices (NDVI and SR) from the remote imaging were not predictive of hybrid differences in yields or whole plant N content at R6. From these results we concluded genetic potential exists among current maize germplasm for NIE breeding improvements, but that more in-depth investigations were needed into other surrogate measures of relevant N efficiency traits in hybrid comparisons. </div><div><br></div><div>Next, hyperspectral imaging was investigated as a potential predictor of agronomic parameters related to N Use Efficiency (NUE, understood here as grain yield relative to applied N fertilizer input). Hyperspectral vegetation indices (HSI) were used to extract the image features for predicting N concentration (whole plant N at R6, %N), Nitrogen Conversion Efficiency (biomass per unit of plant N at R6, NCE), and NIE. Images were collected at V16/V18 and R1/R2 by manned aircraft and unmanned aerial vehicles (UAVs) at 50 cm spatial resolution. Nine maize hybrids, or a subset, were grown under N stress conditions with two plant densities over three site years in either 2014 or 2017. Forty HSI-based mixed models were analyzed for their predictability relative to the ground reference values of %N, NCE, and NIE. Two biomass and structural indices (HBSI1_682,855 and HBSI2_682,910 at R1) were predictive of NCE values and capably differentiated the highest and lowest ranked NCE hybrids. The highest prediction accuracy for NIE was achieved by two biochemical indices (HBCI_8515,550 at both V16 and R1, and HBCI9_490,550 at R1). These also allowed for hybrid differentiation of the highest and lowest ranked NIE hybrids. From these results, we concluded that accurate end-season prediction of hybrid differences in NCE and NIE was possible from mid-season hyperspectral imaging (yet not for %N). Furthermore, the quality of the predictions was dependent on image timing, actual HSI, and the targeted N parameter at maturity. </div><div><br></div><div>One benefit to hyperspectral imaging is that it can provide greater discrimination of imaged materials through its narrow, contiguous bands. However, the data are highly correlated in some ranges. This problem was mitigated through the use of partial least squares regressions (PLSR) to predict the three N parameters from the field data described previously. Data were divided into train and test; then ten-fold cross validation was performed. The twelve models evaluated included those with 89 image bands of 5 nm widths and a selected, reduced set of hyperspectral bands as predictors. The key findings were that PLSR models based on V16 and R1 images provided accurate predictions for final whole-plant %N (R² = 0.73, CV = 11%; R² = 0.68, CV = 10%) and NCE at R6 (R² = 0.71, CV = 10%; R² = 0.73, CV = 12%) but not NIE. Additionally, accurate hybrid differentiation was possible with the combination of hyperspectral imaging and PLSR at R1 to predict %N and NCE values at R6 stage. </div><div><br></div><div>The PLSR and HSI results from this research showed that hyperspectral imaging has the potential for prediction of NUE parameters through non-destructive remote sensing at a broad scale. Additional validation is needed through the study of other genotypes and locations. Nevertheless, practical application of these methods through the integration into early stage breeding programs may allow the early identification of the highest and lowest ranked hybrids providing data-driven decisions for selecting genotypes. Implementation of improved imaging approaches may drive the increased development of maize hybrids with superior NUE. </div><div><br></div>