Hyperspektral avbildning: algoritmiske fremskritt innen variabelt utvalg og anvendelser til trevitenskap

According to Beer’s Law there is a linear dependence between the absorbance of a material and the concentration of an absorbing species in the material. Thus, if one is interested in modeling the concentration of an absorbing species, it should be possible to do so by utilizing a linear model to describe the concentration of the species from a measurement of the absorbance of the material. This thesis is concerned with developing such models from hyperspectral measurements taken in the visible (vis) and near infrared (NIR) region of the electromagnetic spectrum. When developing such models, it is frequently the case that a majority of the wavelengths within a measured spectrum are not absorbed by the species of interest - and should therefore preferably be excluded from the developed model in order to optimize its performance. The process of identifying unnecessary wavelengths is often driven by trial and error, as such it tends to be time consuming and computationally demanding. During the work leading up to Paper I we discovered a conceptually very simple technique which allows calculations to be recycled when developing partial least squares (PLS) models from different combinations of wavelengths. The technique can greatly reduce the computational cost of ftting multiple regression models with various combinations of included/excluded wavelengths to a dataset. In Paper II we incorporate the fndings of Paper I into a genetic algorithm (GA) and demonstrate that the technique also can be used to simultaneously evaluate— in a computationally effcient manner—combinations of wavelengths which are preprocessed using different techniques. In Paper III and IV we develop models which solve wood science related issues. In Paper III samples of spruce (Picea abies) treated with a phosphorus-based fame retardant compound were scanned using a NIR hyperspectral camera. The resulting data was subsequently used to develop a PLS model which estimated the phosphorous content from the spectral signal. In Paper IV samples of thermally modified pine (Pinus sylvestris) were repeatedly scanned over time as they dried. The resulting time series sequences of hyperspectral NIR data was used to develop a regression model capable of estimating the moisture content of the pine from the spectra. In Paper V a generic method is developed for studying and summarizing hyperspectral time series sequences in terms of known and unknown variations. The main idea of the presented method is that spectral variations of known origin are removed from the data. The remaining residual data, containing variation of unknown origin, is then subjected to dimensionality reduction in order to identify new previously unknown variations in the data; variations which in the case of hyperspectral time series data may exhibit temporal as well as spatial patterns of interest. The developed concept was experimentally evaluated in Paper V on a piece of unmodified spruce (Picea abies) which was monitored using a vis-NIR hyperspectral camera as it dried over the course of 21 hours

Latent Variable Models with Applications to Spectral Data Analysis

Recent technological advances in automatic data acquisition have created an ever increasing need to extract meaningful information from huge amount of data. Multivariate predictive models have become important statistical tools in solving modern engineering problems. The purpose of this thesis is to develop novel predictive methods based on latent variable models and validate these methods by applying them into spectral data analysis. In this thesis, hybrid models of principal components regression (PCR) and partial least squares regression (PLS) is proposed. The basic idea of hybrid models is to develop more accurate prediction techniques by combining the merits of PCR and PLS. In the hybrid models, both principal components in PCR and latent variables in PLS are involved in the common regression process. Another major contribution of this work is to propose the robust probabilistic multivariate calibration model (RPMC) to overcome the drawback of Gaussian assumption in most latent variable models. The RPMC was designed to be robust to outliers by adopting a Student-t distribution instead of the Gaussian distribution. An efficient Expectation- Maximization algorithm was derived for parameter estimation in the RPMC. It can also be shown that some popular latent variables such as probabilistic PCA (PPCA) and supervised probabilistic PCA (SPPCA) are special cases of the RPMC. Both the predictive models developed in this thesis were assessed on the real-life spectral data datasets. The hybrid models were applied into the shaft misalignment prediction problem and the RPMC are tested on the near-infrared (NIR) dataset. For the classification problem on the NIR data, the fusion of the regularized discriminant analysis (RDA) and principal components analysis (PCA) was also proposed. The experimental results have shown the effectiveness and efficiency of the proposed methods

Dual state-parameter estimation of hydrological models using ensemble Kalman filter

Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. © 2004 Elsevier Ltd. All rights reserved

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies

Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands

Pine wilt disease (PWD) is a very destructive forest disease that causes the mortality of pine. The infected trees usually die within three months, and the disease spreads fast with the long-horned beetle as the medium if the infected trees are not removed from the forest in time. Therefore, detecting the infected trees at different infection stage, especially the early infection, is crucial for preventing PWD spread. This study aims to exhibit the spectral differences of the pine needles between healthy pines and infected pines at different infection stages and reveal the diagnostic spectral bands for classifying the different infected stage trees. We collected needle samples from healthy, early-, middle-, late-stage infected trees in a Japanese pine (Pinus densiflora) forest and a Korean pine (Pinus koraiensis) forest in northern China to explore the spectral and biochemical properties differences of these four classes, and selected the sensitive bands combining competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA). The selected bands were used for the four infection stages classification by linear discriminant analysis (LDA) algorithm. The results show that Chlorophyll a, chlorophyll b, carotenoids, and moisture content decreases with the aggravation of infection. The green (510–530 nm), red-edge (680–760 nm), and short-wave infrared (1400–1420 nm and 1925–1965 nm) bands are the sensitive bands, and the overall accuracy is 77 % and 78 % for the Japanese pine and Korean pine respectively when using these bands for classifying healthy, early-, middle-, late-stage infected trees. The results demonstrate that physiological parameters including Chlorophyll a, chlorophyll b, carotenoids, and moisture content can be used as the diagnostic parameters of PWD, and the selected sensitive spectral bands are feasible for detecting the stress symptoms of the Japanese pine and Korean pine