Nonlinear Joint Fusion and Detection of Mines Using Multisensor Data

Abstract

This report describes a new nonlinear joint fusion and anomaly detection technique for mine detection applications using two different types of sensor data (synthetic aperture radar [SAR] and hyperspectral sensor [HS] data). A well-known anomaly detector called the "RX algorithm" is first extended to perform fusion and detection simultaneously at the pixel level by appropriately concatenating the information from the two sensors. This approach is then extended to its nonlinear version. The nonlinear fusion-detection approach is based on the statistical kernel learning theory which explicitly exploits the higher-order dependencies (nonlinear relationships) between the two types of sensor data through an appropriate kernel. Experimental results for detecting anomalies (mines) in hyperspectral imagery are presented for linear and nonlinear joint fusion and detection for a co-registered SAR and HS imagery. The results show that the nonlinear techniques outperform linear versions.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2008
Accession Number
ADA485298

Entities

People

  • Nasser M. Nasrabadi

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Detection
  • Detectors
  • False Alarms
  • High Resolution
  • Hyperspectral Imagery
  • Kernel Functions
  • Learning
  • Military Research
  • Multisensors
  • Physical Properties
  • Radar
  • Sensor Fusion
  • Synthetic Aperture Radar
  • Warning Systems

Readers

  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.
  • Wave Propagation and Nonlinear Chaotic Dynamics.