An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Abstract

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

“…Comparisons of mining area classification results based on spectral information with integrated digital elevation model (DEM) and spectral information data reveal superior results from the latter [ 20 ]. Machine-learning algorithms (MLAs) accommodate varied feature sets, with algorithms, such as the support vector machine (SVM) [ 21 , 22 , 23 , 24 ] and random forest (RF) [ 22 ], widely employed for LULC classification in complex mining areas [ 18 , 25 , 26 , 27 , 28 ]. Chen et al [ 29 ] highlighted the importance of obtaining remote sensing features and developing an effective classification model for fine LULC classification.…”

Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification

“…Pixel-based and object-based methods can be used in land cover classification. Kwan et al [29] observed that red-green-blue (RGB) and near infrared (NIR) bands can perform better than the NDVI and EVI approaches in vegetation detection based on land cover classification. For land cover classification, Kwan et al [30] used a limited number of bands (RGB + NIR and RGB + NIR + LiDAR) and EMAP-based augmentation; moreover, they gave a comprehensive performance evaluation of two CNN-based deep learning (customized CNN and CNN-3D) algorithms.…”

Hyperspectral Image Classification Based on Sparse Superpixel Graph

“…Multi-scale covariance maps were introduced to solve the overfitting problem of convolutional neural networkbased methods in HSI classification [26]. Additionally, the existing works show that in the classification process, the combination of spatial information with dimensionality reduction [27], sparse representation [28,29], low rank representation [30], convolutional neural network [31] and various techniques do contribute to improving the classification accuracy.…”