Automated Target Detection and Grouping From Remotely Sensed Data

Abstract

Targets are objects that rise above the bottom surface more than a specific amount defined by IHO survey standards. Currently target detection requires time consuming analysis by the human expert. The contracted task is to design and implement an automated target detector that can be used as a tool by human experts. We tested the implementation internally and have sent some of the test results to experts at NAVO for assessment. In addition we also designed and implemented a target grouping procedure that clusters the targets according to a proximity metric. The resulting grouping can be used to produce polygon outlines that will replace selected clusters of densely spaced targets. Several issues and possible improvements were identified from our testing and analysis. They include alternative algorithms for target identification, computational optimization and parallelization of the implementation, application of machine learning algorithms for optimization of parameters for target identification, and a systematic testing regime.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA484526

Entities

People

  • Brian Bonnlander
  • Choh M. Teng
  • Clark Glymour

Organizations

  • Florida Institute for Human and Machine Cognition

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Cell Size
  • Classification
  • Computations
  • Data Management
  • Detection
  • Detectors
  • Grids
  • Identification
  • Latitude
  • Longitude
  • Machine Learning
  • Operating Systems
  • Sea Level
  • Standards
  • Target Detection
  • Three Dimensional

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Oceanography.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects