Prof. Nasser M. Nasrabadi
Professor
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (89)

Proceedings Article | 18 May 2020
Proc. SPIE. 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
KEYWORDS: Infrared imaging, Near infrared, Super resolution, Detection and tracking algorithms, Airborne remote sensing, Network architectures

Proceedings Article | 5 May 2020
Proc. SPIE. 11394, Automatic Target Recognition XXX
KEYWORDS: Infrared imaging, Feature extraction, Image classification, Target recognition, Automatic target recognition, Medium wave, Forward looking infrared

Proceedings Article | 14 May 2019
Proc. SPIE. 10988, Automatic Target Recognition XXIX
KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Image segmentation, Computing systems, Image classification, Target recognition, Automatic target recognition, Evolutionary algorithms, Classification systems

Proceedings Article | 10 May 2019
Proc. SPIE. 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
KEYWORDS: Super resolution, Convolutional neural networks, Detection and tracking algorithms, Neural networks, Airborne remote sensing

Proceedings Article | 10 May 2019
Proc. SPIE. 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
KEYWORDS: Target detection, Infrared sensors, Infrared imaging, Visible radiation, Sensors, Neural networks, Infrared radiation, Target recognition, Forward looking infrared, Network architectures

Showing 5 of 89 publications
Proceedings Volume Editor (12)

Showing 5 of 12 publications
Conference Committee Involvement (53)
Automatic Target Recognition XXXI
11 April 2021 | Orlando, Florida, United States
Optics and Photonics for Information Processing XIV
26 August 2020 | Online Only, California, United States
Automatic Target Recognition XXX
27 April 2020 | Online Only, California, United States
Optics and Photonics for Information Processing XIII
13 August 2019 | San Diego, California, United States
Automatic Target Recognition XXIX
15 April 2019 | Baltimore, Maryland, United States
Showing 5 of 53 Conference Committees
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. <p> </p> 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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