This course focuses on teaching the methods for detecting minor target signals in hyperspectral imaging. The learning outcomes include understanding three detection methods: generalized least squares (GLS) target detection, GLS and extended least squares (ELS) iterative detection, and whitened principal components analysis (WPCA) targeted anomaly detection. The course covers the mathematics behind these approaches and demonstrates how clutter can be locally modeled for flexible and adaptive detection. The teaching method involves theoretical explanations and examples for each approach. The intended audience for this course includes individuals interested in hyperspectral imaging, anomaly detection, and signal processing.
Anomaly Detection in Hyperspectral Imaging - Neal Gallagher
Chemometrics & Machine Learning in Copenhagen via YouTube
Overview
Syllabus
Intro
Why Hyperspectral Imaging? • Useful for detection of small signals in heterogenous samples where quantities of contaminants may be low on a volume basi but may dominate signal in a single pixel, and for
PCA Anomaly Detection
Anomaly Detection Summary
T² is a Weighting
Example of GLS Weighting
Detection Algorithms
GLS Target Detection Example Signal from the unadulterated wheat gluten is highly variable and
56 ppm Example
Iterative De-Weighting
De-Weight Target by Clutter
200 ppm Melamine in Wheat Gluter
GLS Target Detection Summary
De-Weight vs Orthogonalize
Targeted Anomaly Detection
Hidden Watermark
Section 9
Conclusions
Taught by
Chemometrics & Machine Learning in Copenhagen
