LEADER 03979nam 2200817z- 450 001 9910576871603321 005 20220621 035 $a(CKB)5720000000008460 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84556 035 $a(oapen)doab84556 035 $a(EXLCZ)995720000000008460 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in Object and Activity Detection in Remote Sensing Imagery 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (170 p.) 311 08$a3-0365-4229-9 311 08$a3-0365-4230-2 330 $aThe recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms. 606 $aHistory of engineering & technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $a3D simulation 610 $aadaptive dynamic refined single-stage transformer detector 610 $aair-to-ground synchronization 610 $aarbitrary-oriented object detection in satellite optical imagery 610 $aconvolutional neural network (CNN) 610 $across-view matching 610 $acrowd estimation 610 $adeep learning 610 $adeep learning (DL) 610 $adrone 610 $adynamic feature refinement 610 $afeature pyramid network (FPN) 610 $afeature pyramid transformer 610 $agreen view index (GVI) 610 $ahabitat identification 610 $ainvasive species 610 $amulti-camera system 610 $amultiview semantic vegetation index 610 $an/a 610 $aquad feature pyramid network (Quad-FPN) 610 $aRGB vegetation index 610 $asemantic segmentation 610 $aship detection 610 $asimilarity algorithm for water extraction 610 $aspace alignment 610 $aspatiotemporal feature map 610 $asynthetic aperture radar (SAR) 610 $asynthetic crowd data 610 $athermal imaging 610 $atidal flat water 610 $aUAV-assisted calibration 610 $aunmanned aerial vehicle 610 $aurban forestry 610 $aurban vegetation 610 $aview-invariant description 610 $aYOLOv3 615 7$aHistory of engineering & technology 615 7$aTechnology: general issues 700 $aUlhaq$b Anwaar$4edt$01323487 702 $aGomes$b Douglas Pinto Sampaio$4edt 702 $aUlhaq$b Anwaar$4oth 702 $aGomes$b Douglas Pinto Sampaio$4oth 906 $aBOOK 912 $a9910576871603321 996 $aAdvances in Object and Activity Detection in Remote Sensing Imagery$93035615 997 $aUNINA