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Articles 121 - 150 of 160

Full-Text Articles in Electrical and Computer Engineering

Automated Synthetic Scene Generation, Ryan N. Givens Sep 2014

Automated Synthetic Scene Generation, Ryan N. Givens

Theses and Dissertations

First principles, physics-based models help organizations developing new remote sensing instruments anticipate sensor performance by enabling the ability to create synthetic imagery for proposed sensor before a sensor is built. One of the largest challenges in modeling realistic synthetic imagery, however, is generating the spectrally attributed, three-dimensional scenes on which the models are based in a timely and affordable fashion. Additionally, manual and semi-automated approaches to synthetic scene construction which rely on spectral libraries may not adequately capture the spectral variability of real-world sites especially when the libraries consist of measurements made in other locations or in a lab. This …

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Detection Of Parkinson Disease Rest Tremor, Matthew J. Johnson Aug 2014

Detection Of Parkinson Disease Rest Tremor, Matthew J. Johnson

McKelvey School of Engineering Graduate Student Theses & Dissertations

Parkinson Disease (PD) is a debilitating and progressive movement disorder that is estimated to affect over six million worldwide. One of the most characteristic symptoms of PD is resting tremor, which involves unintentional and rhythmic muscle oscillations of an afflicted extremity while the muscles of said extremity are relaxed. This study involved measuring the rest tremor of 10 PD subjects, 10 Essential Tremor subjects, and 10 healthy control subjects using two devices. One device was an FDA approved accelerometry system to measure human tremor known as the TremorometerTM and the other was a consumer three-dimensional camera known as the …

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Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra Jan 2014

Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra

Doctoral Dissertations

“Melanoma causes more deaths than any other form of skin cancer. Early melanoma detection is important to prevent progression to a more deadly stage. Automated computer-based identification of melanoma from dermoscopic images of skin lesions is the most efficient method in early diagnosis. An automated melanoma identification system must include multiple steps, involving lesion segmentation, feature extraction, feature combination and classification. In this research, a classifier-based approach for automatically selecting a lesion border mask for segmentation of dermoscopic skin lesion images is presented. A logistic regression based model selects a single lesion border mask from multiple border masks generated by …

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A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou Jan 2014

A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou

Turkish Journal of Electrical Engineering and Computer Sciences

The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …

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Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu Jan 2014

Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in …

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Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya Jan 2014

Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository's web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier's performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and …

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Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan Jan 2014

Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a classification system in which learning, feature selection, and classification for incomplete data are simultaneously carried out in an online manner. Learning is conducted on a predefined model including the class-dependent mean vectors and correlation coefficients, which are obtained by incrementally processing the incoming observations with missing features. A nearest neighbor with a Gaussian mixture model, whose parameters are also estimated from the trained model, is used for classification. When a testing observation is received, the algorithm discards the missing attributes on the observation and ranks the available features by performing feature selection on the model that …

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End-To-End Classification Process For The Exploitation Of Vibrometry Data, Ashley Nicole Smith Jan 2014

End-To-End Classification Process For The Exploitation Of Vibrometry Data, Ashley Nicole Smith

Browse all Theses and Dissertations

Laser vibrometry provides a method to identify running vehicles' unique signatures using non-contact measurements. A vehicle's engine, size, materials, shape, and other attributes affect its vibration signature. To develop the capability to classify and identify these signatures, a robust aided target recognition (AiTR) end-to-end process is evaluated and expanded. The main challenge in classifying a vehicle's vibration signatures is presented by the operating conditions and parameters that vary as a function of sensor, environment, and collection locations on the target, among others. Some of the parameters affecting the vibration signatures include weather, terrain, sensor location, sensor type, and engine speed. …

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A Method For Non-Invasive, Automated Behavior Classification In Mice, Using Piezoelectric Pressure Sensors, Steven R. Gooch Jan 2014

A Method For Non-Invasive, Automated Behavior Classification In Mice, Using Piezoelectric Pressure Sensors, Steven R. Gooch

Theses and Dissertations--Electrical and Computer Engineering

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well …

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A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş Jan 2014

A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş

Turkish Journal of Electrical Engineering and Computer Sciences

This paper considers robust classification as a constrained optimization problem. Where the constraints are nonlinear, inequalities defining separating surfaces, whose half spaces include or exclude the data depending on their classes and the cost, are used for attaining robustness and providing the minimum volume regions specified by the half spaces of the surfaces. The constraints are added to the cost using penalty functions to get an unconstrained problem for which the gradient descent method can be used. The separating surfaces, which are aimed to be found in this way, are optimal in the input data space in contrast to the …

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A Ga-Svm Hybrid Classifier For Multiclass Fault Identification Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao Jan 2014

A Ga-Svm Hybrid Classifier For Multiclass Fault Identification Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao

Department of Electrical and Computer Engineering: Faculty Publications

This paper presents a genetic algorithm (GA)- support vector machine (SVM) hybrid classifier for multiclass fault identification of drivetrain gearboxes in variable-speed operational conditions. An adaptive feature extraction algorithm is employed to effectively extract the features of gearbox faults from the stator current signal of an AC machine connected to the gearbox. The multiclass GA-SVM classifier is used to identify the faults in the gearbox according to the fault features extracted. A GA is designed to find the optimal parameters of the SVM to obtain the best classification accuracy. The proposed hybrid classifier is validated on a gearbox connected with …

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New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan Oct 2013

New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan

Open Access Dissertations

When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …

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Wind Field Classification For Satellite Scatterometer Fieldwise Wind Estimation, Charles G. Brown, Dr. David G. Long Sep 2013

Wind Field Classification For Satellite Scatterometer Fieldwise Wind Estimation, Charles G. Brown, Dr. David G. Long

Journal of Undergraduate Research

Since near-surface ocean winds affect global climate, several space-borne instruments have been deployed to measure ocean winds. The most widely known of these instruments are the satellite scatterometers, such as the Seasat Scatterometer (SASS), the NASA Scatterometer (NSCAT), and the Active Microwave Instruments (AMI) aboard the European Remote Sensing satellites ERS-1 and ERS-2. All of these devices measure near-surface ocean wind by transmitting microwave radiation to the ocean’s surface and then measuring the power level of the radiation scattered back to the satellite. The recorded power measurements provide information on the roughness of the ocean’s surface. Since wind alters the …

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Hybrid Of Genetic Algorithm And Great Deluge Algorithm For Rough Set Attribute Reduction, Najmeh Sadat Jaddi, Salwani Abdullah Jan 2013

Hybrid Of Genetic Algorithm And Great Deluge Algorithm For Rough Set Attribute Reduction, Najmeh Sadat Jaddi, Salwani Abdullah

Turkish Journal of Electrical Engineering and Computer Sciences

The attribute reduction problem is the process of reducing unimportant attributes from a decision system to decrease the difficulty of data mining or knowledge discovery tasks. Many algorithms have been used to optimize this problem in rough set theory. The genetic algorithm (GA) is one of the algorithms that has already been applied to optimize this problem. This paper proposes 2 kinds of memetic algorithms, which are a hybridization of the GA, with 2 versions (linear and nonlinear) of the great deluge (GD) algorithm. The purpose of this hybridization is to investigate the ability of this local search algorithm to …

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Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz Jan 2013

Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz

Turkish Journal of Electrical Engineering and Computer Sciences

A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis …

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A Rule Induction Algorithm For Knowledge Discovery And Classification, Ömer Akgöbek Jan 2013

A Rule Induction Algorithm For Knowledge Discovery And Classification, Ömer Akgöbek

Turkish Journal of Electrical Engineering and Computer Sciences

Classification and rule induction are key topics in the fields of decision making and knowledge discovery. The objective of this study is to present a new algorithm developed for automatic knowledge acquisition in data mining. The proposed algorithm has been named RES-2 (Rule Extraction System). It aims at eliminating the pitfalls and disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. In this study, 15 datasets consisting of multiclass values with …

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Psychological Behavior Analysis Using Advanced Signal Processing Techniques For Fmri Data, Charisma Dionne Edwards Jan 2013

Psychological Behavior Analysis Using Advanced Signal Processing Techniques For Fmri Data, Charisma Dionne Edwards

LSU Doctoral Dissertations

Psychological analysis related to voluntary reciprocal trust games were obtained using functional magnetic resonance imaging (fMRI) hyperscanning for 44 pairs of strangers throughout 36 trust games (TG) and 16 control games (CG). Hidden Markov models (HMMs) are proposed to train and classify the fMRI data acquired from these brain regions and extract the essential features of the initial decision of the first player to trust or not trust the second player. These results are evaluated using the different versions of the multifold cross-validation technique and compared to other speech data and other advanced signal processing techniques including linear classification, support …

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Adaptive Feature Extraction And Svm Classification For Real-Time Fault Diagnosis Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao Jan 2013

Adaptive Feature Extraction And Svm Classification For Real-Time Fault Diagnosis Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao

Department of Electrical and Computer Engineering: Faculty Publications

Drivetrain gearboxes play an important role in many modern industrial applications. This paper presents a novel method consisting of adaptive feature extraction and support vector machine (SVM)-based classification for condition monitoring and fault diagnosis of drivetrain gearboxes operating in variable-speed conditions. An adaptive signal resampling algorithm, a frequency tracker, and a feature generation algorithm are integrated in the proposed method for effective extraction of the features of gearbox faults from the stator current signal of the AC electric machine connected to the gearbox. A radial basis function kernel-SVM classifier is designed to identify the fault in the gearbox according to …

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Low-Power Analog Processing For Sensing Applications: Low-Frequency Harmonic Signal Classification, Daniel J. White, Peter E. Williams, Michael W. Hoffman, Sina Balkir Jan 2013

Low-Power Analog Processing For Sensing Applications: Low-Frequency Harmonic Signal Classification, Daniel J. White, Peter E. Williams, Michael W. Hoffman, Sina Balkir

Department of Electrical and Computer Engineering: Faculty Publications

A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fourier Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to …

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Power Transmission Line Fault Classification Using Support Vector Machines, Zhuokang Jia Dec 2012

Power Transmission Line Fault Classification Using Support Vector Machines, Zhuokang Jia

Masters Theses

Electrical transmission lines are prone to faults and failures. When a fault occurs, it is impossible most of the times to fix it manually. Many methods have been adopted in the past in order to serve the purpose as fault diagnosing application. In this thesis, I discuss the method of Support Vector Machine (SVM) for fault diagnosis. SVM has the edge of good generalization over other fault diagnosing applications because it is based on pattern recognize algorithms. The aim is to classify the type of fault in the lines. Furthermore, in this work, the current and voltage of each phase …

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Study Of Statistical And Computational Intelligence Methods Of Detecting Temporal Signature Of Forest Fire Heat Plume From Single-Band Ground-Based Infrared Video, Daniel G. Kohler Jun 2012

Study Of Statistical And Computational Intelligence Methods Of Detecting Temporal Signature Of Forest Fire Heat Plume From Single-Band Ground-Based Infrared Video, Daniel G. Kohler

Master's Theses

This thesis will analyze video from land-based, cooled mid-wave infrared cameras to identify temporal features indicative of a heat plume from a forest fire. Desirable features and methods will show an ability to distinguish between heat plume movement and other movements, such as foliage, vehicles, humans, and birds in flight. Features will be constructed primarily using combinations of statistics and principal component analysis (PCA) with intent to detect key characteristics of fire and heat plume: persistence and growth. Several classification systems will combine and filter the features in an attempt to classify pixels as either heat or non-heat. The classification …

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Design And Implementation Of A Byzantine Fault Tolerance Framework For Non-Deterministic Applications, H. Zhang, Wenbing Zhao, Louise E. Moser, P. Michael Melliar-Smith Jun 2011

Design And Implementation Of A Byzantine Fault Tolerance Framework For Non-Deterministic Applications, H. Zhang, Wenbing Zhao, Louise E. Moser, P. Michael Melliar-Smith

Electrical and Computer Engineering Faculty Publications

State-machine-based replication is an effective way to increase the availability and dependability of mission-critical applications. However, all practical applications contain some degree of non-determinism. Consequently, ensuring strong replica consistency in the presence of application non-determinism has been one of the biggest challenges in building dependable distributed systems. In this Study, the authors propose a classification of common types of application non-determinism with respect to the requirement of achieving Byzantine fault tolerance (BFT), and present the design and implementation of a BFT framework that controls these types of non-determinism in a systematic manner.

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Two-Class Classification With Various Characteristics Based On Kernel Principal Component Analysis And Support Vector Machines, Ivanna Kristianti Timotius, Iwan Setyawan, Andreas Ardian Febrianto Apr 2011

Two-Class Classification With Various Characteristics Based On Kernel Principal Component Analysis And Support Vector Machines, Ivanna Kristianti Timotius, Iwan Setyawan, Andreas Ardian Febrianto

Makara Journal of Technology

Two class pattern classification problems appeared in many applications. In some applications, the characteristic of the members in a class is dissimilar. This paper proposed a classification system for this problem. The proposed system was developed based on the combination of kernel principal component analysis (KPCA) and support vector machines (SVMs). This system has been implemented in a two class face recognition problem. The average of the classification rate in this face image classification is 82.5%.

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Psychophysical Similarity Based Feature Selection For Nodule Retrieval In Ct, Ravi K. Samala Jan 2011

Psychophysical Similarity Based Feature Selection For Nodule Retrieval In Ct, Ravi K. Samala

Open Access Theses & Dissertations

The emerging paradigms in cancer research indicate the need for a multi-perspective and multi-modal screening approach for early lung cancer diagnosis to increase the probability of clinical resection. Currently no standalone screening methodology is proved to suffice for a clinical diagnosis. Computed tomography has been proved to present abnormality at an early stage with less impact on survival rate in population studies. Nevertheless, because of its non-invasive characteristic, it can be used for diagnosis, prognosis and visualization of tumor. Studies have shown that Computer aided diagnosis (CAD) as a second reader can perform in a similar capacity as a radiologist. …

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Measuring Traffic Flow And Classifying Vehicle Types: A Surveillance Video Based Approach, Erhan İnce Jan 2011

Measuring Traffic Flow And Classifying Vehicle Types: A Surveillance Video Based Approach, Erhan İnce

Turkish Journal of Electrical Engineering and Computer Sciences

The paper presents a vehicle counting method based on invariant moments and shadow aware foreground masks. Estimation of the background and the segmentation of foreground regions can be done using either the Mixture of Gaussians model (MoG) or an improved version of the Group Based Histogram (GBH) technique. The work demonstrates that, even though the improved GBH method delivers performance just as good as MoG, considering computational efficiency, MoG is more suitable. Shadow aware binary masks for each frame are formed by using background subtraction and shadow removal in the Hue Saturation and Value (HSV) domain. To determine new vehicles …

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Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea Jan 2011

Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea

Open Access Theses & Dissertations

The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution …

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Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi Nov 2009

Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi

Chemistry Faculty Research & Creative Works

Objective: To identify and analyze subtypes of globules based on size, shape, network connectedness, pigmentation, and distribution to determine which globule types and globule distributions are most frequently associated with a diagnosis of malignant melanoma. Design: Retrospective case series of dermoscopy images with globules. Setting: Private dermatology practices. Participants: Patients in dermatology practices. Intervention: Observation only. Main Outcome Measure: Association of globule types with malignant melanoma. Results: The presence of large globules (odds ratio [OR], 5.25) and globules varying in size (4.72) or shape (5.37) had the highest ORs for malignant melanoma among all globule types and combinations studied. Classical …

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Temporal Data Classification Using Linear Classifiers, Peter Revesz, Thomas Triplet Sep 2009

Temporal Data Classification Using Linear Classifiers, Peter Revesz, Thomas Triplet

School of Computing: Conference and Workshop Papers

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality.

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Seasonal Adaptation Of Vegetation Color In Satellite Images For Flight Simulations, Yuzhong Shen, Jiang Li, Vamsi Mantena, Srinivas Jakkula Jan 2009

Seasonal Adaptation Of Vegetation Color In Satellite Images For Flight Simulations, Yuzhong Shen, Jiang Li, Vamsi Mantena, Srinivas Jakkula

Electrical & Computer Engineering Faculty Publications

Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper proposes a novel method that identifies vegetative areas in satellite images and then alters vegetation color to simulate seasonal changes based on training image pairs. The proposed method first generates a vegetation map for pixels corresponding to vegetative areas, using ISODATA clustering and vegetation classification. The ISODATA algorithm determines the number of clusters automatically. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. Six features are then computed …

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Reduced Dimensionality Hyperspectral Classification Using Finite Mixture Models, Vikram Jayaram Jan 2009

Reduced Dimensionality Hyperspectral Classification Using Finite Mixture Models, Vikram Jayaram

Open Access Theses & Dissertations

Classification of HSI data is a challenging problem for two main reasons. First, with limited spatial resolution of HSI sensors and/or the distance of the observed scene, the images invariably contain pixels composed of several materials. It is desirable to resolve the contributions of the constituents from the observed image without relying on high spatial resolution images. Remote sensing cameras have been designed to capture a wide spectral range motivating the use of post-processing techniques to distinguish materials via their spectral signatures. Secondly, available training data for most pattern recognition problems in HSI processing is severely inadequate. Under the framework …

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