LEADER 04846nam 2201093z- 450 001 9910557148403321 005 20210501 035 $a(CKB)5400000000040574 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68306 035 $a(oapen)doab68306 035 $a(EXLCZ)995400000000040574 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial Neural Networks and Evolutionary Computation in Remote Sensing 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (256 p.) 311 08$a3-03943-827-1 311 08$a3-03943-828-X 330 $aArtificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification. 606 $aResearch and information: general$2bicssc 610 $aaerial images 610 $aAI on the edge 610 $aartificial neural networks 610 $aChina 610 $aclassification 610 $aclassification ensemble 610 $aCNN 610 $aCNNs 610 $aconvolutional neural network 610 $aconvolutional neural networks 610 $aconvolutional neural networks (CNNs) 610 $adeep learning 610 $adense network 610 $adigital terrain analysis 610 $adilated convolutional network 610 $aearth observation 610 $aend-to-end detection 610 $aFaster RCNN 610 $afeature fusion 610 $aFeicheng 610 $afew-shot learning 610 $aGaofen 6 610 $aGaofen-2 imagery 610 $ageographic information system (GIS) 610 $ahyperspectral image classification 610 $ahyperspectral images 610 $aimage downscaling 610 $aimage segmentation 610 $aland-use 610 $aLiDAR 610 $alight detection and ranging 610 $amachine learning 610 $amask R-CNN 610 $amask regional-convolutional neural networks 610 $amicrosat 610 $amission 610 $amixed forest 610 $amixed-inter nonlinear programming 610 $amodel generalization 610 $amulti-label segmentation 610 $amulti-scale feature fusion 610 $ananosat 610 $aon-board 610 $aoptical remote sensing images 610 $apost-processing 610 $aquadruplet loss 610 $aremote sensing 610 $aresource extraction 610 $asemantic features 610 $asemantic segmentation 610 $aSentinel-2 610 $aship detection 610 $asingle shot multi-box detector (SSD) 610 $aspatial distribution 610 $aSRGAN 610 $astatistical features 610 $asuper-resolution 610 $asuperstructure optimization 610 $aTai'an 610 $atransfer learning 610 $aunmanned aerial vehicles 610 $awinter wheat 610 $aYou Look Only Once-v3 (YOLO-v3) 615 7$aResearch and information: general 700 $aKavzoglu$b Taskin$4edt$01288742 702 $aKavzoglu$b Taskin$4oth 906 $aBOOK 912 $a9910557148403321 996 $aArtificial Neural Networks and Evolutionary Computation in Remote Sensing$93020968 997 $aUNINA