Priority list of biodiversity metrics to observe from space
Monitoring global biodiversity from space through remotely sensing geospatial patterns has
high potential to add to our knowledge acquired by field observation. Although a framework …
high potential to add to our knowledge acquired by field observation. Although a framework …
[HTML][HTML] Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing
With the goal of advancing remote sensing in biodiversity science, Spectranomics
represents an emerging approach, and a suite of quantitative methods, intended to link plant …
represents an emerging approach, and a suite of quantitative methods, intended to link plant …
Effective data generation for imbalanced learning using conditional generative adversarial networks
Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …
classification algorithms. Although there are different strategies to address this problem …
A framework for evaluating land use and land cover classification using convolutional neural networks
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has …
for many environmental and social applications. The increase in availability of RS data has …
The utility of Random Forests for wildfire severity mapping
Reliable fire severity mapping is a vital resource for fire scientists and land management
agencies globally. Satellite derived pre-and post-fire differenced severity indices (∆ FSI) …
agencies globally. Satellite derived pre-and post-fire differenced severity indices (∆ FSI) …
CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems
The data imbalance problem in classification is a frequent but challenging task. In real-world
datasets, numerous class distributions are imbalanced and the classification result under …
datasets, numerous class distributions are imbalanced and the classification result under …
Using of multi-source and multi-temporal remote sensing data improves crop-type mapping in the subtropical agriculture region
C Sun, Y Bian, T Zhou, J Pan - Sensors, 2019 - mdpi.com
Crop-type identification is very important in agricultural regions. Most researchers in this
area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to …
area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to …
A convolutional neural network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
In this study, we automate tree species classification and mapping using field-based training
data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural …
data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural …
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 …
Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning
Learning from imbalanced datasets is challenging for standard algorithms, as they are
designed to work with balanced class distributions. Although there are different strategies to …
designed to work with balanced class distributions. Although there are different strategies to …