LEADER 03942nam 2200793z- 450 001 9910576871603321 005 20231214133243.0 035 $a(CKB)5720000000008460 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84556 035 $a(EXLCZ)995720000000008460 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Object and Activity Detection in Remote Sensing Imagery 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (170 p.) 311 $a3-0365-4229-9 311 $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 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $amulti-camera system 610 $aspace alignment 610 $aUAV-assisted calibration 610 $across-view matching 610 $aspatiotemporal feature map 610 $aview-invariant description 610 $aair-to-ground synchronization 610 $atidal flat water 610 $aYOLOv3 610 $asimilarity algorithm for water extraction 610 $aarbitrary-oriented object detection in satellite optical imagery 610 $aadaptive dynamic refined single-stage transformer detector 610 $afeature pyramid transformer 610 $adynamic feature refinement 610 $asynthetic aperture radar (SAR) 610 $aship detection 610 $aconvolutional neural network (CNN) 610 $adeep learning (DL) 610 $afeature pyramid network (FPN) 610 $aquad feature pyramid network (Quad-FPN) 610 $acrowd estimation 610 $a3D simulation 610 $aunmanned aerial vehicle 610 $asynthetic crowd data 610 $ainvasive species 610 $athermal imaging 610 $ahabitat identification 610 $adeep learning 610 $adrone 610 $amultiview semantic vegetation index 610 $aurban forestry 610 $agreen view index (GVI) 610 $asemantic segmentation 610 $aurban vegetation 610 $aRGB vegetation index 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 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