Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …
A review of deep learning used in the hyperspectral image analysis for agriculture
C Wang, B Liu, L Liu, Y Zhu, J Hou, P Liu… - Artificial Intelligence …, 2021 - Springer
Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can
capture up to several hundred images of different wavelengths and offer relevant spectral …
capture up to several hundred images of different wavelengths and offer relevant spectral …
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks
The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-
flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods …
flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods …
Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)
Z Hao, L Lin, CJ Post, EA Mikhailova, M Li… - ISPRS Journal of …, 2021 - Elsevier
Tree-crown and height are primary tree measurements in forest inventory. Convolutional
neural networks (CNNs) are a class of neural networks, which can be used in forest …
neural networks (CNNs) are a class of neural networks, which can be used in forest …
Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images
B Zhang, L Zhao, X Zhang - Remote Sensing of Environment, 2020 - Elsevier
Airborne hyperspectral remote sensing data with both rich spectral and spatial features can
effectively improve the classification accuracy of vegetation species. However, the spectral …
effectively improve the classification accuracy of vegetation species. However, the spectral …
Individual tree-crown detection and species identification in heterogeneous forests using aerial RGB imagery and deep learning
Automatic identification and mapping of tree species is an essential task in forestry and
conservation. However, applications that can geolocate individual trees and identify their …
conservation. However, applications that can geolocate individual trees and identify their …
Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using …
The classification of tree species can significantly benefit from high spatial and spectral
information acquired by unmanned aerial vehicles (UAVs) associated with advanced …
information acquired by unmanned aerial vehicles (UAVs) associated with advanced …
Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data
Understanding species distribution and canopy structure of mangrove forests is imperative
for flora and fauna conservation in mangrove habitats. However, most mangrove studies …
for flora and fauna conservation in mangrove habitats. However, most mangrove studies …
[HTML][HTML] Detecting and mapping tree crowns based on convolutional neural network and Google Earth images
Mapping tree crown is critical for estimating the functional and spatial distribution of
ecosystem services. However, accurate and up-to-date urban crown mapping remains a …
ecosystem services. However, accurate and up-to-date urban crown mapping remains a …
[HTML][HTML] UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series
Increasing tree mortality due to climate change has been observed globally. Remote
sensing is a suitable means for detecting tree mortality and has been proven effective for the …
sensing is a suitable means for detecting tree mortality and has been proven effective for the …