Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19298
Title: Vegetation detection using deep learning and conventional methods
Authors: Ayhan, Bulent 
Kwan, Chiman 
Budavari, Bence 
Kwan, Liyun 
Lu, Yan 
Perez, Daniel 
Li, Jiang 
Skarlatos, Dimitrios 
Vlachos, Marinos 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: CNN;Deep learning;DeepLabV3+;Machine learning;NDVI;Vegetation
Issue Date: 1-Aug-2020
Source: Remote Sensing, 2020, vol. 12, no. 15, articl. no. 2502
Volume: 12
Issue: 15
Journal: Remote Sensing 
Abstract: Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.
URI: https://hdl.handle.net/20.500.14279/19298
ISSN: 20724292
DOI: 10.3390/RS12152502
Rights: © by the authors.
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : Applied Research LLC 
Old Dominion University 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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