Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral Imagery
Abstract
The rapid expansion of remote sensing and information collection capabilities demands methods to highlight interesting or anomalous patterns within an overabundance of data. This research addresses this issue for hyperspectral imagery (HSI). Two new reconstruction based HSI anomaly detectors are outlined: one using principal component analysis (PCA), and the other a form of non-linear PCA called logistic principal component analysis. Two very effective, yet relatively simple, modifications to the autonomous global anomaly detector are also presented, improving algorithm performance and enabling receiver operating characteristic analysis. A novel technique for HSI anomaly detection dubbed "multiple PCA" is introduced and found to perform as well or better than existing detectors on HYDICE data while using only linear deterministic methods. Finally, a response surface based optimization is performed on algorithm parameters such as to affect consistent desired algorithm performance.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 27, 2014
- Accession Number
- ADA601028
Entities
People
- James A. Jablonski
Organizations
- Air Force Institute of Technology