Using Deep Convolutional Neural Networks to Classify Littoral Areas With 3-Band and 5-Band Imagery

Abstract

With the assistance of high-resolution satellites, unmanned aerial vehicles, and fixed camera observation points, coastal change detection and landscape classification are active research areas that have the capability to provide situational awareness. However, classification of bottom types in littoral waters is an area of coastal landscape classification that has not been studied extensively, and accurate and timely classification of bottom types remains elusive. Furthermore, it is unclear whether 5-band imagery (RGB, or red, green, blue; along with near infrared and RedEdge) will help deep convolutional neural networks (DCNN) classify bottom types easier than just color (RGB). In this study, a DJI Inspire unmanned aerial vehicle equipped with a MicaSense RedEdge sensor was used to obtain 5-band imagery of several coastal areas. These images were classified by various means for six areas: swash zone, sandy bottom, bottom other than sand, sand, kelp and above ground rock. This database was then used to train the DCNN for classification on unseen imagery. The models were first initialized with RGB data and then compared to the 5-band outputs. DCNNs were able to classify littoral areas with more accuracy using 5-band imagery than 3-band imagery. Further studies can apply the methods developed in this research and compare 5-band imagery obtained from unmanned aerial systems with imagery obtained from high-resolution satellites such as WorldView 3.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2020
Accession Number
AD1114260

Entities

People

  • Ashley M. Mielke

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence Software
  • Computational Science
  • Computer Languages
  • Convolutional Neural Networks
  • Dimensionality Reduction
  • Feature Extraction
  • Information Science
  • Machine Learning
  • Near Infrared Radiation
  • Neural Networks
  • Remote Sensing
  • Seabed
  • Supervised Machine Learning
  • Unmanned Aerial Systems
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Coastal Oceanography
  • Neural Network Machine Learning.

Technology Areas

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