Longwave Infrared Hyperspectral Subpixel Target Detection with Machine Learning (Preprint)

Abstract

Hyperspectral imaging has been used to perform automated material detection and identification. However, traditional detection methods based on statistical data processing produce a higher than desired false alarm rate for subpixel targets due to violated assumptions. This paper compares performance of machine learning methods using neural networks in detecting subpixel targets with traditional statistical methods. The assessment will utilize airborne data collected by the SEBASS sensor under a variety of atmospheric conditions from a number of different altitudes. Various methods for atmospheric compensation and temperature-emissivity separation will be used as well to assess robustness of the detection approaches.

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Document Details

Document Type
Technical Report
Publication Date
May 18, 2020
Accession Number
AD1099311

Entities

People

  • Jacob A. Martin
  • Joseph Meola
  • Seung H. An

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Altitude
  • Detection
  • Detectors
  • Emissivity
  • False Alarms
  • Hyperspectral Imagery
  • Information Science
  • Learning
  • Machine Learning
  • Materials
  • Neural Networks
  • Statistical Algorithms
  • Statistics
  • Target Detection
  • Warning Systems

Readers

  • Image Processing and Computer Vision.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks