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 …
Recent advances of hyperspectral imaging technology and applications in agriculture
Remote sensing is a useful tool for monitoring spatio-temporal variations of crop
morphological and physiological status and supporting practices in precision farming. In …
morphological and physiological status and supporting practices in precision farming. In …
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Abstract Google Earth Engine (GEE) is a cloud-based geospatial processing platform for
large-scale environmental monitoring and analysis. The free-to-use GEE platform provides …
large-scale environmental monitoring and analysis. The free-to-use GEE platform provides …
Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …
classification during the past two decades. Among these machine learning algorithms …
Representation-enhanced status replay network for multisource remote-sensing image classification
Deep-learning-based methods are widely used in multisource remote-sensing image
classification, and the improvement in their performance confirms the effectiveness of deep …
classification, and the improvement in their performance confirms the effectiveness of deep …
Map** 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 …
Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review
Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …
images, we have witnessed a growing interest of the remote sensing community in …
Very deep convolutional neural networks for complex land cover map** using multispectral remote sensing imagery
Despite recent advances of deep Convolutional Neural Networks (CNNs) in various
computer vision tasks, their potential for classification of multispectral remote sensing …
computer vision tasks, their potential for classification of multispectral remote sensing …
Spatially and thematically explicit information of wetlands is important to understanding
ecosystem functions and services, as well as for establishment of management policy and …
ecosystem functions and services, as well as for establishment of management policy and …
Hyperspectral and multispectral classification for coastal wetland using depthwise feature interaction network
The monitoring of coastal wetlands is of great importance to the protection of marine and
terrestrial ecosystems. However, due to the complex environment, severe vegetation …
terrestrial ecosystems. However, due to the complex environment, severe vegetation …