Prof. Nasser M. Nasrabadi
Professor
SPIE Involvement:
Fellow status | Conference Program Committee | Conference Chair | Author | Instructor
Publications (96)

PROCEEDINGS ARTICLE | April 30, 2018
Proc. SPIE. 10648, Automatic Target Recognition XXVIII
KEYWORDS: Target detection, Convolutional neural networks, Detection and tracking algorithms, Sensors, Databases, Image segmentation, Target recognition, Automatic target recognition, Forward looking infrared, Model-based design

PROCEEDINGS ARTICLE | May 12, 2016
Proc. SPIE. 9844, Automatic Target Recognition XXVI
KEYWORDS: Long wavelength infrared, Mid-IR, Detection and tracking algorithms, Data modeling, Databases, Feature extraction, Neural networks, Target recognition, Automatic target recognition, Forward looking infrared

PROCEEDINGS ARTICLE | May 9, 2012
Proc. SPIE. 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
KEYWORDS: Target detection, Hyperspectral imaging, Detection and tracking algorithms, Data modeling, Dubnium, Sensors, Chemical species, Associative arrays, Reconstruction algorithms, Hyperspectral target detection

PROCEEDINGS ARTICLE | May 20, 2011
Proc. SPIE. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
KEYWORDS: Target detection, Hyperspectral imaging, Detection and tracking algorithms, Sensors, Chemical species, Single mode fibers, Associative arrays, Hyperspectral target detection, Statistical modeling, Astatine

PROCEEDINGS ARTICLE | May 20, 2011
Proc. SPIE. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
KEYWORDS: Target detection, Hyperspectral imaging, Optical filters, Optical sensors, Principal component analysis, Detection and tracking algorithms, Sensors, Matrices, Single mode fibers, Hyperspectral target detection

PROCEEDINGS ARTICLE | May 5, 2011
Proc. SPIE. 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII
KEYWORDS: Visible radiation, Detection and tracking algorithms, Databases, Chemical species, Synthetic aperture radar, Image processing, Associative arrays, Image classification, Target recognition, Automatic target recognition

Showing 5 of 96 publications
Conference Committee Involvement (50)
Optics and Photonics for Information Processing XIII
11 August 2019 | San Diego, California, United States
Automatic Target Recognition XXIX
14 April 2019 | Baltimore, Maryland, United States
Optics and Photonics for Information Processing XII
19 August 2018 | San Diego, California, United States
Automatic Target Recognition XXVIII
16 April 2018 | Orlando, Florida, United States
Optics and Photonics for Information Processing XI
7 August 2017 | San Diego, California, United States
Showing 5 of 50 published special sections
Course Instructor
SC995: Target Detection Algorithms for Hyperspectral Imagery
This course provides a broad introduction to the basic concept of automatic target and object detection and its applications in Hyperspectral Imagery (HSI). The primary goal of this course is to introduce the well known target detection algorithms in hyperspectral imagery. Examples of the classical target detection techniques such as spectral matched filter, subspace matched filter, adaptive matched filter, orthogonal subspace, support vector machine (SVM) and machine learning are reviewed. Construction of invariance subspaces for target and background as well as the use of regularization techniques are presented. Standard atmospheric correction and compensation techniques are reviewed. Anomaly detection techniques for HSI and dual band FLIR imagery are also discussed. Applications of HSI for detection of mines, targets, humans, chemical plumes and anomalies are reviewed.
SC1222: Deep Learning and Its Applications in Image Processing
This course provides a broad introduction to the basic concept of the classical neural networks (NN) and its current evolution to deep learning (DL) technology. The primary goal of this course is to introduce the well-known deep learning architectures and their applications in image processing for object detection, identification, verification, action recognition, scene understanding and biometrics using a single modality or multimodality sensor information. This course will describe the history of neural networks and its progress to current deep learning technology. It covers several DL architectures such the classical multi-layer feed forward neural networks, convolutional neural networks (CNN), restricted Boltzmann machines (RBM), auto-encoders and recurrent neural networks such as long term short memory (LSTM). Use of deep learning architectures for feature extraction and classification will be described and demonstrated. Examples of popular CNN-based architectures such as AlexNet, VGGNet, GooGleNet (inception modules), ResNet, DeepFace, Highway Networks, FractalNet and their applications to defense and security will be discussed. Advanced architectures such as Siamese deep networks, coupled neural networks, auto-encoders, fusion of multiple CNNs and their applications to object verification and classification will also be covered.
SC1215: Deep Learning Architectures for Defense and Security
This course provides a broad introduction to the basic concept of the classical neural networks (NN) and its current evolution to deep learning (DL) technology. The primary goal of this course is to introduce the well-known deep learning architectures and their applications in defense and security for object detection, identification, verification, action recognition, scene understanding and biometrics using a single modality or multimodality sensor information. This course will describe the history of neural networks and its progress to current deep learning technology. It covers several DL architectures such the classical multi-layer feed forward neural networks, convolutional neural networks (CNN), restricted Boltzmann machines (RBM), auto-encoders and recurrent neural networks such as long term short memory (LSTM). Use of deep learning architectures for feature extraction and classification will be described and demonstrated. Examples of popular CNN-based architectures such as AlexNet, VGGNet, GooGleNet (inception modules), ResNet, DeepFace, Highway Networks, FractalNet and their applications to defense and security will be discussed. Advanced architectures such as Siamese deep networks, coupled neural networks, auto-encoders, fusion of multiple CNNs and their applications to object verification and classification will also be covered.
SC491: Neural Networks Applications in Image Processing
This course provides a broad introduction to the basic concepts of artificial neural networks and its applications in image processing. A large number of neural network architectures and their training algorithms are reviewed. Examples of neural networks architectures that are covered in this course are single layer perceptrons, multilayer perceptrons, time-delay neural networks, Kohonen feature maps, learning vector quantization, radial basis function and Hopfield neural networks. An introduction to support vector machine and learning theory is provided. Applications that are covered are object and pattern recognition, object inspection, classifiers, handwritten word and digit recognition, automatic target recognition, and image compression.
SC186: Automatic Target Recognition Using Artificial Neural Networks
This course introduces the basic concepts of using artificial neural networks for Automatic Target Recognition (ATR). Neural network-based ATR algorithms are reviewed. Sensors covered are forward-looking infrared (FLIR), synthetic aperture radar (SAR), laser radar, and high resolution sonar imagery. Neural network-based detection (cueing), clutter false alarm rejection, feature extraction, recognition, classification, segmentation and enhancement techniques are described. Clutter representation and characterization are discussed. Sensor fusion algorithms based on neural network techniques are also reviewed.
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