Compressive Hyperspectral Imaging and Anomaly Detection

Abstract

We have developed and applied successfully new algorithms for hyperspectral imagery. These include compressive sensing, anomaly detection, target detection, endmember detection, unmixing and change detection. These were tested on data provided by AFRL with good results, including change detection under different lightning conditions. Ideas involved Bregman iteration applied to L1 and total variation based optimizations were used and also successfully applied to subsampled data. A nonnegative matrix factorization and completion algorithm was introduced which allows the reconstruction of partially observed or corrupted hyperspectral data. A surprising spinoff is sparse reconstruction of offshore oil spills based on multispectral measurements.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2013
Accession Number
ADA580327

Entities

People

  • Kevin F. Kelly
  • Pradeep Thiyanarantnam
  • Stanley Osher
  • Susan Chen
  • Wotao Yin

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Change Detection
  • Compressed Sensing
  • Computational Fluid Dynamics
  • Computer Vision
  • Data Analysis
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • Hyperspectral Imagery
  • Matched Filters
  • Materials
  • Oil Spills
  • Target Detection
  • Warning Systems

Readers

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