Vegetation Detection Using Deep Learning and Conventional Methods

Yan Lu,Jiang Li,Marinos Vlachos,Daniel Perez,Dimitrios Skarlatos,Chiman Kwan,Bence Budavari,Liyun Kwan,Bulent Ayhan

doi:10.3390/rs12152502

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.

Highlights

Land cover classification [1] has been widely used in change detection monitoring [2], construction surveying [3], agricultural management [4], green vegetation classification [5], identifying emergency landing sites for UAVs [6,7], biodiversity conservation [8], land-use [9], and urban planning [10]
In the DeepLabV3+ results shown in Figure 7a,b, green color pixels correspond to tree/shrub/grass, the silver color corresponds to the barren land and the red color corresponds to urban land)
Comparing the deep learning and normalized difference vegetation index (NDVI)-based approaches, we observe that the NDVI-machine learning (ML) method provided significantly better results than the two deep learning methods

Summary

Introduction

Land cover classification [1] has been widely used in change detection monitoring [2], construction surveying [3], agricultural management [4], green vegetation classification [5], identifying emergency landing sites for UAVs [6,7], biodiversity conservation [8], land-use [9], and urban planning [10]. In Skarlatos et al [3], chlorophyll-rich vegetation detection was a crucial stepping stone to improve the accuracy of the estimated digital terrain model (DTM). Upon detection of vegetation areas, they were automatically removed from the digital surface model (DSM) to have better DTM estimates. Bradley et al [11] conducted chlorophyll-rich vegetation detection to improve autonomous navigation in natural environments for autonomous mobile robots operating in off-road terrain. Some conventional vegetation detection methods are based on normalized difference vegetation index (NDVI) [14,15,16], which takes advantage of different solar radiation absorption phenomena of green plants in the red spectral and near-infrared spectral regions [17,18]

Methods

Results

Discussion

Conclusion