Hyperspectral Anomaly Detection Algorithm
This project is about an anomaly detection algorithm for ground-to-ground, or air-to-ground, software applications requiring automatic target detection making use of hyperspectral (HS) data. A data transformation approach is unveiled to be utilised by the two-sample data structure univariate semiparametric and nonparametric scoring. We have discussed about Hyperspectral Sensing Model, Semiparametric (SemiP) Anomaly Detection, HS Data Transformation, etc.