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dc.contributor.authorLee, Saro*
dc.contributor.authorJung, Hyung-Sup*
dc.date.accessioned2021-02-11T18:29:31Z
dc.date.available2021-02-11T18:29:31Z
dc.date.issued2019*
dc.date.submitted2019-12-09 11:49:15*
dc.identifier42509*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/52518
dc.description.abstractAs 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.*
dc.languageEnglish*
dc.subjectTJ1-1570*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.otherartificial neural network*
dc.subject.othern/a*
dc.subject.othermodel switching*
dc.subject.othersensitivity analysis*
dc.subject.otherneural networks*
dc.subject.otherlogit boost*
dc.subject.otherQaidam Basin*
dc.subject.otherland subsidence*
dc.subject.otherland use/land cover (LULC)*
dc.subject.othernaïve Bayes*
dc.subject.othermultilayer perceptron*
dc.subject.otherconvolutional neural networks*
dc.subject.othersingle-class data descriptors*
dc.subject.otherlogistic regression*
dc.subject.otherfeature selection*
dc.subject.othermapping*
dc.subject.otherparticulate matter 10 (PM10)*
dc.subject.otherBayes net*
dc.subject.othergray-level co-occurrence matrix*
dc.subject.othermulti-scale*
dc.subject.otherLogistic Model Trees*
dc.subject.otherclassification*
dc.subject.otherPanax notoginseng*
dc.subject.otherlarge scene*
dc.subject.othercoarse particle*
dc.subject.othergrayscale aerial image*
dc.subject.otherGaofen-2*
dc.subject.otherenvironmental variables*
dc.subject.othervariable selection*
dc.subject.otherspatial predictive models*
dc.subject.otherweights of evidence*
dc.subject.otherlandslide prediction*
dc.subject.otherrandom forest*
dc.subject.otherboosted regression tree*
dc.subject.otherconvolutional network*
dc.subject.otherVietnam*
dc.subject.othermodel validation*
dc.subject.othercolorization*
dc.subject.otherdata mining techniques*
dc.subject.otherspatial predictions*
dc.subject.otherSCAI*
dc.subject.otherunmanned aerial vehicle*
dc.subject.otherhigh-resolution*
dc.subject.othertexture*
dc.subject.otherspatial sparse recovery*
dc.subject.otherlandslide susceptibility map*
dc.subject.othermachine learning*
dc.subject.otherreproducible research*
dc.subject.otherconstrained spatial smoothing*
dc.subject.othersupport vector machine*
dc.subject.otherrandom forest regression*
dc.subject.othermodel assessment*
dc.subject.otherinformation gain*
dc.subject.otherALS point cloud*
dc.subject.otherbagging ensemble*
dc.subject.otherone-class classifiers*
dc.subject.otherleaf area index (LAI)*
dc.subject.otherlandslide susceptibility*
dc.subject.otherlandsat image*
dc.subject.otherionospheric delay constraints*
dc.subject.otherspatial spline regression*
dc.subject.otherremote sensing image segmentation*
dc.subject.otherpanchromatic*
dc.subject.otherSentinel-2*
dc.subject.otherremote sensing*
dc.subject.otheroptical remote sensing*
dc.subject.othermateria medica resource*
dc.subject.otherGIS*
dc.subject.otherprecise weighting*
dc.subject.otherchange detection*
dc.subject.otherTRMM*
dc.subject.othertraffic CO*
dc.subject.othercrop*
dc.subject.othertraining sample size*
dc.subject.otherconvergence time*
dc.subject.otherobject detection*
dc.subject.othergully erosion*
dc.subject.otherdeep learning*
dc.subject.otherclassification-based learning*
dc.subject.othertransfer learning*
dc.subject.otherlandslide*
dc.subject.othertraffic CO prediction*
dc.subject.otherhybrid model*
dc.subject.otherwinter wheat spatial distribution*
dc.subject.otherlogistic*
dc.subject.otheralternating direction method of multipliers*
dc.subject.otherhybrid structure convolutional neural networks*
dc.subject.othergeoherb*
dc.subject.otherpredictive accuracy*
dc.subject.otherreal-time precise point positioning*
dc.subject.otherspectral bands*
dc.titleMachine Learning Techniques Applied to Geoscience Information System and Remote Sensing*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-216-3*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039212156*
oapen.relation.isbn9783039212163*
oapen.pages438*
oapen.edition1st*


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