TY  - GEN
AU  - Lee,Saro
AU  - Jung,Hyung-Sup
TI  - Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
SN  - books978-3-03921-216-3
PY  - 2019///
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - artificial neural network
KW  - n/a
KW  - model switching
KW  - sensitivity analysis
KW  - neural networks
KW  - logit boost
KW  - Qaidam Basin
KW  - land subsidence
KW  - land use/land cover (LULC)
KW  - naïve Bayes
KW  - multilayer perceptron
KW  - convolutional neural networks
KW  - single-class data descriptors
KW  - logistic regression
KW  - feature selection
KW  - mapping
KW  - particulate matter 10 (PM10)
KW  - Bayes net
KW  - gray-level co-occurrence matrix
KW  - multi-scale
KW  - Logistic Model Trees
KW  - classification
KW  - Panax notoginseng
KW  - large scene
KW  - coarse particle
KW  - grayscale aerial image
KW  - Gaofen-2
KW  - environmental variables
KW  - variable selection
KW  - spatial predictive models
KW  - weights of evidence
KW  - landslide prediction
KW  - random forest
KW  - boosted regression tree
KW  - convolutional network
KW  - Vietnam
KW  - model validation
KW  - colorization
KW  - data mining techniques
KW  - spatial predictions
KW  - SCAI
KW  - unmanned aerial vehicle
KW  - high-resolution
KW  - texture
KW  - spatial sparse recovery
KW  - landslide susceptibility map
KW  - machine learning
KW  - reproducible research
KW  - constrained spatial smoothing
KW  - support vector machine
KW  - random forest regression
KW  - model assessment
KW  - information gain
KW  - ALS point cloud
KW  - bagging ensemble
KW  - one-class classifiers
KW  - leaf area index (LAI)
KW  - landslide susceptibility
KW  - landsat image
KW  - ionospheric delay constraints
KW  - spatial spline regression
KW  - remote sensing image segmentation
KW  - panchromatic
KW  - Sentinel-2
KW  - remote sensing
KW  - optical remote sensing
KW  - materia medica resource
KW  - GIS
KW  - precise weighting
KW  - change detection
KW  - TRMM
KW  - traffic CO
KW  - crop
KW  - training sample size
KW  - convergence time
KW  - object detection
KW  - gully erosion
KW  - deep learning
KW  - classification-based learning
KW  - transfer learning
KW  - landslide
KW  - traffic CO prediction
KW  - hybrid model
KW  - winter wheat spatial distribution
KW  - logistic
KW  - alternating direction method of multipliers
KW  - hybrid structure convolutional neural networks
KW  - geoherb
KW  - predictive accuracy
KW  - real-time precise point positioning
KW  - spectral bands
N1  - Open Access
N2  - As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing
UR  - https://mdpi.com/books/pdfview/book/1533
UR  - https://directory.doabooks.org/handle/20.500.12854/52518
ER  -