Change Detection Of Marine Environments Using Machine Learning

Abstract

Change detection in aerial imagery is important for many disciplines. The Navy and Marine Corps have used it for planning missions in coastal areas. Current change-detection methods analyze registered images pixel by pixel. We trained two convolutional neural networks, VGG19 and DenseNet121, to distinguish eight coastal classes using 8,000 oblique aerial images. We then used both commercial satellite images and orthorectified imagery from small unmanned aerial systems with structure from-motion methods to test whether learning could transfer from the oblique images. Our results showed significant effects of the tile size for coastal classification in the orthorectified images. We also tested change detection by comparing the highest confidence coastal class between images taken in different time periods, but this was often unsuccessful due to the errors in the classifications.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2020
Accession Number
AD1114662

Entities

People

  • Theodore A. Iii Ayoub

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Automata Theory
  • California
  • Change Detection
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Data Mining
  • Dimensionality Reduction
  • Geography
  • Image Processing
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Satellite Imaging
  • United States

Fields of Study

  • Environmental science

Readers

  • Coastal and Marine Engineering/Sediment Transport/Hydraulic Engineering
  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

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