Target Detection Algorithms for Hyperspectral Imagery

Level: Introductory Length: 7 hours Format: In-Person Lecture Intended Audience: Scientists, engineers, technicians, or managers who wish to learn more about target detection in hyperspectral, multispectral or dual-band FLIR imagery. Undergraduate training in engineering or science is assumed. Description: 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. Learning Outcomes: This course will enable you to: - describe the fundamental concepts of target detection algorithms as applied to HSI - learn the procedure to use the generalized maximum likelihood ratio test to design spectral detectors - describe the fundamental differences between different detection algorithms based on their model representations - develop statistical models as well as subspace models for HSI data - explain the difference between anomaly detection and classification - distinguish between linear and nonlinear approaches (SVM and Kernel learning techniques) - develop anomaly detection techniques for different environmental scenarios - describe linear models and unmixing techniques for abundance measures - plot ROC curves to evaluate the performance of the algorithms Instructor(s): Nasser M. Nasrabadi is a professor in the Lane Computer Science and Electrical Engineering Department at West Virginia University. He was senior research scientist (ST) at US Army Research Laboratory (ARL). He is actively engaged in research in deep learning, image processing, automatic target recognition and hyperspectral imaging for defense and security. He has published over 300 papers in journals and conference proceedings. He has been an associate editor for the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions for Neural Networks. He is a Fellow of IEEE and SPIE. Event: SPIE Defense + Commercial Sensing 2016 Course Held: 17 April 2016