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dc.contributor.editorJeon, Gwanggil
dc.date.accessioned2023-01-05T12:34:16Z
dc.date.available2023-01-05T12:34:16Z
dc.date.issued2022
dc.identifierONIX_20230105_9783036560830_47
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/95818
dc.description.abstractThe reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TQ Environmental science, engineering & technology
dc.subject.otherpine wilt disease dataset
dc.subject.otherGIS application visualization
dc.subject.othertest-time augmentation
dc.subject.otherobject detection
dc.subject.otherhard negative mining
dc.subject.othervideo synthetic aperture radar (SAR)
dc.subject.othermoving target
dc.subject.othershadow detection
dc.subject.otherdeep learning
dc.subject.otherfalse alarms
dc.subject.othermissed detections
dc.subject.othersynthetic aperture radar (SAR)
dc.subject.otheron-board
dc.subject.othership detection
dc.subject.otherYOLOv5
dc.subject.otherlightweight detector
dc.subject.otherremote sensing image
dc.subject.otherspectral domain translation
dc.subject.othergenerative adversarial network
dc.subject.otherpaired translation
dc.subject.othersynthetic aperture radar
dc.subject.othership instance segmentation
dc.subject.otherglobal context modeling
dc.subject.otherboundary-aware box prediction
dc.subject.otherland-use and land-cover
dc.subject.otherbuilt-up expansion
dc.subject.otherprobability modelling
dc.subject.otherlandscape fragmentation
dc.subject.othermachine learning
dc.subject.othersupport vector machine
dc.subject.otherfrequency ratio
dc.subject.otherfuzzy logic
dc.subject.otherartificial intelligence
dc.subject.otherremote sensing
dc.subject.otherinterferometric phase filtering
dc.subject.othersparse regularization (SR)
dc.subject.otherdeep learning (DL)
dc.subject.otherneural convolutional network (CNN)
dc.subject.othersemantic segmentation
dc.subject.otheropen data
dc.subject.otherbuilding extraction
dc.subject.otherunet
dc.subject.otherdeeplab
dc.subject.otherclassifying-inversion method
dc.subject.otherAIS
dc.subject.otheratmospheric duct
dc.subject.othership detection and classification
dc.subject.otherrotated bounding box
dc.subject.otherattention
dc.subject.otherfeature alignment
dc.subject.otherweather nowcasting
dc.subject.otherResNeXt
dc.subject.otherradar data
dc.subject.otherspectral-spatial interaction network
dc.subject.otherspectral-spatial attention
dc.subject.otherpansharpening
dc.subject.otherUAV visual navigation
dc.subject.otherSiamese network
dc.subject.othermulti-order feature
dc.subject.otherMIoU
dc.subject.otherimbalanced data classification
dc.subject.otherdata over-sampling
dc.subject.othergraph convolutional network
dc.subject.othersemi-supervised learning
dc.subject.othertroposcatter
dc.subject.othertropospheric turbulence
dc.subject.otherintercity co-channel interference
dc.subject.otherconcrete bridge
dc.subject.othervisual inspection
dc.subject.otherdefect
dc.subject.otherdeep convolutional neural network
dc.subject.othertransfer learning
dc.subject.otherinterpretation techniques
dc.subject.otherweakly supervised semantic segmentation
dc.subject.othern/a
dc.titleArtificial Intelligence-Based Learning Approaches for Remote Sensing
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-6084-7
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036560830
oapen.relation.isbn9783036560847
oapen.pages382
oapen.place.publicationBasel


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