Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system

Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system

Deep learning networks have shown great success in several computer vision applications, but its implementation in natural land cover mapping in the context of object-based image analysis (OBIA) is rarely explored area especially in terms of the impact of training sample size on the performance comp...

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Bibliographic Details
Main Authors: Tao Liu, Amr Abd-Elrahman, Jon Morton, Victor L. Wilhelm
Format: Article
Language:English
Published: Taylor & Francis Group 2018-03-01
Series:GIScience & Remote Sensing
Subjects:
fcn
deep learning
convolutional neural network
obia
uas
Online Access:http://dx.doi.org/10.1080/15481603.2018.1426091

Internet

http://dx.doi.org/10.1080/15481603.2018.1426091

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