Missile Detection System (MDS) is one of the main components of a self-protection system developed against the threat of guided missiles for airborne platforms. The requirements such as time critical operation and high accuracy in classification performance make the &lsquo / Pattern Recognition&rsquo / problem of an MDS a hard task. Problem can be defined in two main parts such as &lsquo / Feature Set Evaluation&rsquo / (FSE) and &lsquo / Classifier&rsquo / designs. The main goal of feature set evaluation is to employ a dimensionality reduction process for the input data set, while not disturbing the classification performance in the result. In this thesis study, FSE approaches are investigated for the pattern recognition problem of a generic MDS. First, synthetic data generation is carried out in software environment by employing generic models and assumptions in order to reflect the nature of a realistic problem environment. Then, data sets are evaluated in order to draw a baseline for further feature set evaluation approaches. Further, a theoretical background including the concepts of Class Separability, Feature Selection and Feature Extraction is given. Several widely used methods are assessed in terms of convenience for the problem by giving necessary justifications depending on the data set characteristics. Upon this background, software implementations are performed regarding several feature set evaluation techniques. Simulations are carried out in order to process dimensionality reduction. For the evaluation of the resulting data sets in terms of classification performance, software implementation of a classifier is realized. Resulting classification performances of the applied approaches are compared and evaluated.

In this thesis the main objective is to design a machine vision system that classifies aflatoxin contaminated chili peppers from uncontaminated ones in a rapid and non-destructive manner via hyperspectral imaging and machine learning techniques. Hyperspectral image series of chili pepper samples collected from different regions of Turkey have been acquired under halogen and UV illuminations. A novel feature set based on quantized absolute difference of consecutive spectral band features is proposed. Spectral band energies along with absolute difference energies of the consecutive spectral bands are utilized as features and compared with other feature extraction methods such as Teager energy operator and 2D wavelet Linear Discriminant Bases (2D-LDB). For feature selection, Fisher discrimination power, information theoretic Minimum Redundancy Maximum Relevance (mRMR) method and proposed Multi Layer Perceptron (MLP) based feature selection schemes are utilized.Finally, Linear Discriminant Classifier (LDC), Support Vector Machines (SVM) and MLP are used as classifiers. It is observed that MLP outperforms other learning models in terms of predictor performance. We verified the performance and robustness of our proposed methods on different real world datasets. It is suggested that to achieve high classification accuracy and predictor robustness, a machine vision system with halogen excitation and quantized absolute difference of consecutive spectral band features should be utilized.

<p>Emerging mobile mapping systems include a wide range of platforms, for instance, manned aircraft, unmanned aerial vehicles (UAV), terrestrial systems like trucks, tractors, robots, and backpacks, that can carry multiple sensors including LiDAR scanners, cameras, and georeferencing units. Such systems can maneuver in the field to quickly collect high-resolution data, capturing detailed information over an area of interest. With the increased volume and distinct characteristics of the data collected, practical quality control procedures that assess the agreement within/among datasets acquired by various sensors/systems at different times are crucial for accurate, robust interpretation. Moreover, the ability to derive semantic information from acquired data is the key to leveraging the complementary information captured by mobile mapping systems for diverse applications. This dissertation addresses these challenges for different systems (airborne and terrestrial), environments (urban and rural), and applications (agriculture, archaeology, hydraulics/hydrology, and transportation).</p> <p>In this dissertation, quality control procedures that utilize features automatically identified and extracted from acquired data are developed to evaluate the relative accuracy between multiple datasets. The proposed procedures do not rely on manually deployed ground control points or targets and can handle challenging environments such as coastal areas or agricultural fields. Moreover, considering the varying characteristics of acquired data, this dissertation improves several data processing/analysis techniques essential for meeting the needs of various applications. An existing ground filtering algorithm is modified to deal with variation in point density; digital surface model (DSM) smoothing and seamline control techniques are proposed for improving the orthophoto quality in agricultural fields. Finally, this dissertation derives semantic information for diverse applications, including 1) shoreline retreat quantification, 2) automated row/alley detection for plant phenotyping, 3) enhancement of orthophoto quality for tassel/panicle detection, and 4) point cloud semantic segmentation for mapping transportation corridors. The proposed approaches are tested using multiple datasets from UAV and wheel-based mobile mapping systems. Experimental results verify that the proposed approaches can effectively assess the data quality and provide reliable interpretation. This dissertation highlights the potential of modern mobile mapping systems to map challenging environments for a variety of applications.</p>