Artificial Neural Networks and Evolutionary Computation in Remote Sensing

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 multidimens...

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Bibliografiske detaljer
Institution som forfatter:	Kavzoglu, Taskin
Format:	Livro
Sprog:	Inglês
Udgivet:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Fag:	bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general convolutional neural network image segmentation multi-scale feature fusion semantic features Gaofen 6 aerial images land-use Tai’an convolutional neural networks (CNNs) feature fusion ship detection optical remote sensing images end-to-end detection transfer learning remote sensing single shot multi-box detector (SSD) You Look Only Once-v3 (YOLO-v3) Faster RCNN statistical features Gaofen-2 imagery winter wheat post-processing spatial distribution Feicheng China light detection and ranging LiDAR deep learning convolutional neural networks CNNs mask regional-convolutional neural networks mask R-CNN digital terrain analysis resource extraction hyperspectral image classification few-shot learning quadruplet loss dense network dilated convolutional network artificial neural networks classification superstructure optimization mixed-inter nonlinear programming hyperspectral images super-resolution SRGAN model generalization image downscaling mixed forest multi-label segmentation semantic segmentation unmanned aerial vehicles classification ensemble machine learning Sentinel-2 geographic information system (GIS) earth observation on-board microsat mission nanosat AI on the edge CNN
Online adgang:	https://directory.doabooks.org/handle/20.500.12854/68306
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing

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