Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size/shape/texture) for classifying urban LULC features, Geographic Obje...

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Bibliographic Details
Main Authors: Shahab Eddin Jozdani, Brian Alan Johnson, Dongmei Chen
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Remote Sensing
Subjects:
remote sensing
high-spatial resolution imagery
deep learning
GEOBIA
land use/cover classification
Online Access:https://www.mdpi.com/2072-4292/11/14/1713

Internet

https://www.mdpi.com/2072-4292/11/14/1713

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