Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios
Abstract
Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation-invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2020
- Accession Number
- AD1144694
Entities
People
- Nicholas M. Westing
Organizations
- Air Force Institute of Technology