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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Detalles Bibliográficos
Autor Corporativo: Kavzoglu, Taskin
Formato: Livro
Lenguaje:Inglês
Publicado: MDPI - Multidisciplinary Digital Publishing Institute 2021
Materias:
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
Acceso en línea:https://directory.doabooks.org/handle/20.500.12854/68306
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