04846nam 2201093z- 450 991055714840332120210501(CKB)5400000000040574(oapen)https://directory.doabooks.org/handle/20.500.12854/68306(oapen)doab68306(EXLCZ)99540000000004057420202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierArtificial Neural Networks and Evolutionary Computation in Remote SensingBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (256 p.)3-03943-827-1 3-03943-828-X Artificial 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.Research and information: generalbicsscaerial imagesAI on the edgeartificial neural networksChinaclassificationclassification ensembleCNNCNNsconvolutional neural networkconvolutional neural networksconvolutional neural networks (CNNs)deep learningdense networkdigital terrain analysisdilated convolutional networkearth observationend-to-end detectionFaster RCNNfeature fusionFeichengfew-shot learningGaofen 6Gaofen-2 imagerygeographic information system (GIS)hyperspectral image classificationhyperspectral imagesimage downscalingimage segmentationland-useLiDARlight detection and rangingmachine learningmask R-CNNmask regional-convolutional neural networksmicrosatmissionmixed forestmixed-inter nonlinear programmingmodel generalizationmulti-label segmentationmulti-scale feature fusionnanosaton-boardoptical remote sensing imagespost-processingquadruplet lossremote sensingresource extractionsemantic featuressemantic segmentationSentinel-2ship detectionsingle shot multi-box detector (SSD)spatial distributionSRGANstatistical featuressuper-resolutionsuperstructure optimizationTai'antransfer learningunmanned aerial vehicleswinter wheatYou Look Only Once-v3 (YOLO-v3)Research and information: generalKavzoglu Taskinedt1288742Kavzoglu TaskinothBOOK9910557148403321Artificial Neural Networks and Evolutionary Computation in Remote Sensing3020968UNINA