LEADER 04871nam 2201261z- 450 001 9910639984703321 005 20251116142526.0 010 $a3-0365-6084-X 035 $a(CKB)5470000001633507 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/95818 035 $a(EXLCZ)995470000001633507 100 $a20202301d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence-Based Learning Approaches for Remote Sensing 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (382 p.) 311 08$a3-0365-6083-1 330 $aThe 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 606 $aEnvironmental science, engineering & technology$2bicssc 610 $apine wilt disease dataset 610 $aGIS application visualization 610 $atest-time augmentation 610 $aobject detection 610 $ahard negative mining 610 $avideo synthetic aperture radar (SAR) 610 $amoving target 610 $ashadow detection 610 $adeep learning 610 $afalse alarms 610 $amissed detections 610 $asynthetic aperture radar (SAR) 610 $aon-board 610 $aship detection 610 $aYOLOv5 610 $alightweight detector 610 $aremote sensing image 610 $aspectral domain translation 610 $agenerative adversarial network 610 $apaired translation 610 $asynthetic aperture radar 610 $aship instance segmentation 610 $aglobal context modeling 610 $aboundary-aware box prediction 610 $aland-use and land-cover 610 $abuilt-up expansion 610 $aprobability modelling 610 $alandscape fragmentation 610 $amachine learning 610 $asupport vector machine 610 $afrequency ratio 610 $afuzzy logic 610 $aartificial intelligence 610 $aremote sensing 610 $ainterferometric phase filtering 610 $asparse regularization (SR) 610 $adeep learning (DL) 610 $aneural convolutional network (CNN) 610 $asemantic segmentation 610 $aopen data 610 $abuilding extraction 610 $aunet 610 $adeeplab 610 $aclassifying-inversion method 610 $aAIS 610 $aatmospheric duct 610 $aship detection and classification 610 $arotated bounding box 610 $aattention 610 $afeature alignment 610 $aweather nowcasting 610 $aResNeXt 610 $aradar data 610 $aspectral-spatial interaction network 610 $aspectral-spatial attention 610 $apansharpening 610 $aUAV visual navigation 610 $aSiamese network 610 $amulti-order feature 610 $aMIoU 610 $aimbalanced data classification 610 $adata over-sampling 610 $agraph convolutional network 610 $asemi-supervised learning 610 $atroposcatter 610 $atropospheric turbulence 610 $aintercity co-channel interference 610 $aconcrete bridge 610 $avisual inspection 610 $adefect 610 $adeep convolutional neural network 610 $atransfer learning 610 $ainterpretation techniques 610 $aweakly supervised semantic segmentation 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 615 7$aEnvironmental science, engineering & technology 700 $aJeon$b Gwanggil$4edt$01279103 702 $aJeon$b Gwanggil$4oth 906 $aBOOK 912 $a9910639984703321 996 $aArtificial Intelligence-Based Learning Approaches for Remote Sensing$93014581 997 $aUNINA