Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

Genetic Algorithms for Feature Selection and Classification of Complex Chromatographic and Spectroscopic Data

A basic methodology for analyzing large multivariate chemical data sets based on feature selection is proposed. Each chromatogram or spectrum is represented as a point in a high dimensional measurement space. A genetic algorithm for feature selection and classification is applied to the data to identify features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. A good principal component plot can only be generated using features whose variance or information is primarily about differences between classes in the data. Hence, feature subsets that maximize the ratio of between-class to within-class variance are selected by the pattern recognition genetic algorithm. Furthermore, the structure of the data set can be explored, for example, new classes can be discovered by simply tuning various parameters of the fitness function of the pattern recognition genetic algorithm. The proposed method has been validated on a wide range of data. A two-step procedure for pattern recognition analysis of spectral data has been developed. First, wavelets are used to denoise and deconvolute spectral bands by decomposing each spectrum into wavelet coefficients, which represent the samples constituent frequencies. Second, the pattern recognition genetic algorithm is used to identify wavelet coefficients characteristic of the class. In several studies involving spectral library searching, this method was employed. In one study, a search pre-filter to detect the presence of carboxylic acids from vapor phase infrared spectra which has previously eluted prominent researchers has been successfully formulated and validated. In another study, this same approach has been used to develop a pattern recognition assisted infrared library searching technique to determine the model, manufacturer, and year of the vehicle from which a clear coat paint smear originated. The pattern recognition genetic algorithm has also been used to develop a potential method to identify molds in indoor environments using volatile organic compounds. A distinct profile indicative of microbial volatile organic compounds was developed from air sampling data that could be readily differentiated from the blank for both high mold count and moderate mold count exposure samples. The utility of the pattern recognition genetic algorithm for discovery of biomarker candidates from genomic and proteomic data sets has also been shown.Chemistry Departmen

Pattern Recognition Assisted Infrared Library Searching

The development of a genetic algorithm (GA) for pattern recognition analysis of infrared spectral data is proposed. The GA selects spectral features that optimize the separation of the different functional groups in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA primarily convey information about differences between classes. Hence, the principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space, since it restricts the search to feature sets whose principal component plots show clustering of the spectra on the basis of chemical structure. In addition, the algorithm focuses on those classes and or samples that are difficult to classify as it trains using a form of boosting to modify class and sample weights. Samples that consistently classify correctly are not as heavily weighted as samples that are more difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a neural network. The proposed GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one -pass procedure for feature selection and pattern recognition.School of Electrical & Computer Engineerin

Chemometric Methods for the Determination of Volatile Organic Compounds with Microsensor Arrays.

This research addresses critical chemometric modeling and data analysis functions needed to guide the design and implementation of novel meso-scale and micro-scale instrumentation that incorporates chromatographic separation with microsensor array detection. The first issue addressed relates to the fidelity of the response pattern generated by an array for a chromatographically resolved analyte to its calibrated reference pattern. A statistically rigorous decision rule was developed that accounts for the inherent variability in the array signals and permits assessments of pattern fidelity at a known rate of error. Building on this first study, a more sophisticated and robust approach to peak purity assessment based on fixed-size moving window factor analysis was adapted and evaluated by simulation. With this approach, a minority component with a peak area 0.5% of the primary component could be detected at a primary-peak signal-to-noise ratio as low as 20:1. To address problems involving partial overlap of chromatographic peaks, a self-modeling curve resolution method was applied, which entails an alternating least square algorithm coupled with evolving factor analysis. Response patterns from an array of four sensors were tested with binary co-elutions to evaluate the resolution of mixture components as a function of random noise, chromatographic separation, pattern similarity, and relative composition. In a separate series of studies, the advantages of multi-transducer sensor arrays over single-transducer arrays for vapor recognition were examined. Starting with a database of sensitivities to 11 vapors from 15 microsensors, it was shown by Monte Carlo simulation and principal component regression modeling that optimal MT arrays consistently outperform ST arrays of similar size, and that with judiciously selected 5-sensor MT arrays one-third of all possible ternary vapor mixtures are reliably discriminated from their individual components and binary component mixtures, whereas none are reliably determined with any of the ST arrays. Using the same database, the limits of recognition were determined for various mixtures, revealing that, in general, mixtures cannot be recognized at relative concentration ratios exceeding 20:1 between two components. Collectively, the research reported here has served to help define the limits of performance and interpret the output of microsensor arrays as components of microanalytical systems.Ph.D.Industrial HealthUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61627/1/jincg_1.pd

Publications of the Jet Propulsion Laboratory July 1965 through July 1966

Bibliography on Jet Propulsion Laboratory technical reports and memorandums, space programs summary, astronautics information, and literature searche

NASA thesaurus. Volume 1: Hierarchical Listing

There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field