A Brief Introduction to the Evaluation of Learned Models for Aerial Object Detection

Abstract

Satellites equipped with imaging sensors represent key assets for intelligence, surveillance, and reconnaissance (ISR) missions. However, the shear volume of data that can potentially be gained from tasking these assets can quickly become too much for humans to manually consider in its entirety. It is for this reason that automated systems for analyzing satellite imagery are vital to aid human analysts in the process of extracting actionable information from satellite imagery.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2022
Accession Number
AD1170660

Entities

People

  • Eric Heim

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Data Mining
  • Data Sets
  • Deep Learning
  • Detectors
  • Failure Mode And Effect Analysis
  • Image Recognition
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Probabilistic Models
  • Satellite Imaging
  • Supervised Machine Learning

Readers

  • Computational Modeling and Simulation
  • Sensor Fusion and Tracking Systems.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • Space
  • Space - Satellites
  • Space - Space Objects