Document Type

Article

Publication Date

2020

DOI

10.3390/rs12152502

Publication Title

Remote Sensing

Volume

12

Issue

15

Pages

23 pp.

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.

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Original Publication Citation

Ayhan, B., Kwan, C., Budavari, B., Kwan, L., Lu, Y., Perez, D., Li, J., Skarlatos, D., & Vlachos, M. (2020). Vegetation detection using deep learning and conventional methods. Remote Sensing, 12(15), 23 pp., Article 2502. https://doi.org/10.3390/rs12152502

Download

Share

COinS