Artificial Neural Networks and Evolutionary Computation in Remote Sensing

• Autore: Kavzoglu Taskin
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (256 p.)
• Soggetto topico: Research and information: general
• Soggetto non controllato: aerial images; AI on the edge; artificial neural networks; China; classification; classification ensemble; CNN; CNNs; convolutional neural network; convolutional neural networks; convolutional neural networks (CNNs); deep learning; dense network; digital terrain analysis; dilated convolutional network; earth observation; end-to-end detection; Faster RCNN; feature fusion; Feicheng; few-shot learning; Gaofen 6; Gaofen-2 imagery; geographic information system (GIS); hyperspectral image classification; hyperspectral images; image downscaling; image segmentation; land-use; LiDAR; light detection and ranging; machine learning; mask R-CNN; mask regional-convolutional neural networks; microsat; mission; mixed forest; mixed-inter nonlinear programming; model generalization; multi-label segmentation; multi-scale feature fusion; nanosat; on-board; optical remote sensing images; post-processing; quadruplet loss; remote sensing; resource extraction; semantic features; semantic segmentation; Sentinel-2; ship detection; single shot multi-box detector (SSD); spatial distribution; SRGAN; statistical features; super-resolution; superstructure optimization; Tai'an; transfer learning; unmanned aerial vehicles; winter wheat; You Look Only Once-v3 (YOLO-v3)
• Persona (resp. second.): KavzogluTaskin
• Sommario/riassunto: 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.
• Titolo autorizzato: Artificial Neural Networks and Evolutionary Computation in Remote Sensing
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557148403321
• Lo trovi qui: Univ. Federico II