04871nam 2201261z- 450 991063998470332120240301170435.03-0365-6084-X(CKB)5470000001633507(oapen)https://directory.doabooks.org/handle/20.500.12854/95818(EXLCZ)99547000000163350720202301d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierArtificial Intelligence-Based Learning Approaches for Remote SensingBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (382 p.)3-0365-6083-1 The 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.Technology: general issuesbicsscHistory of engineering & technologybicsscEnvironmental science, engineering & technologybicsscpine wilt disease datasetGIS application visualizationtest-time augmentationobject detectionhard negative miningvideo synthetic aperture radar (SAR)moving targetshadow detectiondeep learningfalse alarmsmissed detectionssynthetic aperture radar (SAR)on-boardship detectionYOLOv5lightweight detectorremote sensing imagespectral domain translationgenerative adversarial networkpaired translationsynthetic aperture radarship instance segmentationglobal context modelingboundary-aware box predictionland-use and land-coverbuilt-up expansionprobability modellinglandscape fragmentationmachine learningsupport vector machinefrequency ratiofuzzy logicartificial intelligenceremote sensinginterferometric phase filteringsparse regularization (SR)deep learning (DL)neural convolutional network (CNN)semantic segmentationopen databuilding extractionunetdeeplabclassifying-inversion methodAISatmospheric ductship detection and classificationrotated bounding boxattentionfeature alignmentweather nowcastingResNeXtradar dataspectral-spatial interaction networkspectral-spatial attentionpansharpeningUAV visual navigationSiamese networkmulti-order featureMIoUimbalanced data classificationdata over-samplinggraph convolutional networksemi-supervised learningtroposcattertropospheric turbulenceintercity co-channel interferenceconcrete bridgevisual inspectiondefectdeep convolutional neural networktransfer learninginterpretation techniquesweakly supervised semantic segmentationTechnology: general issuesHistory of engineering & technologyEnvironmental science, engineering & technologyJeon Gwanggiledt1279103Jeon GwanggilothBOOK9910639984703321Artificial Intelligence-Based Learning Approaches for Remote Sensing3014581UNINA