Accession Number:

ADA550165

Title:

Object Level HSI-LIDAR Data Fusion for Automated Detection of Difficult Targets

Descriptive Note:

Journal article

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC

Report Date:

2011-10-10

Pagination or Media Count:

15.0

Abstract:

Data fusion from disparate sensors significantly improves automated man-made target detection performance compared to that of just an individual sensor. In particular, it can solve hyperspectral imagery HSI detection problems pertaining to low-radiance man-made objects and objects in shadows. We present an algorithm that fuses HSI and LIDAR data for automated detection of man-made objects. LIDAR is used to define a set of potential targets based on physical dimensions, and HSI is then used to discriminate between man-made and natural objects. The discrimination technique is a novel HSI detection concept that uses an HSI detection score localization metric capable of distinguishing between wide-area score distributions inherent to natural objects and highly localized score distributions indicative of man-made targets. A typical man-made localization score was found to be around 0.5 compared to natural background typical localization scores being less than 0.1.

Subject Categories:

  • Optical Detection and Detectors
  • Target Direction, Range and Position Finding
  • Atomic and Molecular Physics and Spectroscopy

Distribution Statement:

APPROVED FOR PUBLIC RELEASE