Target Detection using Convolutional Neural Networks

Abstract

This research explores the use of Convolutional Neural Networks (CNNs) to classify targets of interest within satellite imagery. Methods were specifically devised for the classification of airports within Landsat-8 scenes. A novel automated dataset generation technique was developed to create labeled datasets from satellite imagery using only coordinate metadata. Using this approach a very large dataset of over132,000 labeled images was created without human input. This dataset was used to evaluate the effects of color and resolution on airport classification accuracy. Two experiments were run with the first experiment classifying large airports with 96.8% accuracy, and the second classifying large and medium airports with 90.2% accuracy. Additionally, a new algorithm was developed which optimizes the selection of multi-spectral color bands in order to best trade-off classification accuracy for the number of spectral bands employed.

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Document Details

Document Type
Technical Report
Publication Date
Mar 23, 2018
Accession Number
AD1056164

Entities

People

  • Robert P. Loibl

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Artificial Satellites
  • Convolutional Neural Networks
  • Detection
  • Detectors
  • Information Science
  • Machine Learning
  • Neural Networks
  • Remote Sensing
  • Satellite Imaging
  • Spacecraft
  • Target Detection
  • United States

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space