USING DEEP CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY LITTORAL AREAS WITH 3-BAND AND 5-BAND IMAGERY
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Authors
Mielke, Ashley M.
Subjects
machine learning
neural networks
bottom type
semantic segmentation
remote sensing
data processing
artificial intelligence
deep learning
unmanned aerial vehicles
Carmel River
unmanned systems
littoral zone
littorals
neural networks
bottom type
semantic segmentation
remote sensing
data processing
artificial intelligence
deep learning
unmanned aerial vehicles
Carmel River
unmanned systems
littoral zone
littorals
Advisors
Orescanin, Mara S.
Date of Issue
2020-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
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.
Type
Thesis
Description
Series/Report No
Department
Oceanography (OC)
Organization
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NPS Report Number
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Funder
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.