Vegetation Detection Using Deep Learning and Conventional Methods

Remote Sensing

卷:12

Liyun Kwan¹  Bence Budavari¹  Bulent Ayhan¹  Chiman Kwan¹  Dimitrios Skarlatos²  Marinos Vlachos²  Daniel Perez³  Jiang Li³  Yan Lu³ 

[1] Applied Research LLC, Rockville, MD 20850, USA;

[2] Cyprus University of Technology, Department of Civil Engineering and Geomatics, P.O. Box 50329, 3603 Limassol, Cyprus;

[3] Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23259, USA;

关键词: NDVI;    deep learning;    machine learning;    vegetation;    DeepLabV3+, CNN;   

DOI  :  10.3390/rs12152502

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown