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Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images
Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images
Autore Bazi Yakoub
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (438 p.)
Soggetto topico Research & information: general
Soggetto non controllato synthetic aperture radar
despeckling
multi-scale
LSTM
sub-pixel
high-resolution remote sensing imagery
road extraction
machine learning
DenseUNet
scene classification
lifting scheme
convolution
CNN
image classification
deep features
hand-crafted features
Sinkhorn loss
remote sensing
text image matching
triplet networks
EfficientNets
LSTM network
convolutional neural network
water identification
water index
semantic segmentation
high-resolution remote sensing image
pixel-wise classification
result correction
conditional random field (CRF)
satellite
object detection
neural networks
single-shot
deep learning
global convolution network
feature fusion
depthwise atrous convolution
high-resolution representations
ISPRS vaihingen
Landsat-8
faster region-based convolutional neural network (FRCNN)
single-shot multibox detector (SSD)
super-resolution
remote sensing imagery
edge enhancement
satellites
open-set domain adaptation
adversarial learning
min-max entropy
pareto ranking
SAR
Sentinel–1
Open Street Map
U–Net
desert
road
infrastructure
mapping
monitoring
deep convolutional networks
outline extraction
misalignments
nearest feature selector
hyperspectral image classification
two stream residual network
Batch Normalization
plant disease detection
precision agriculture
UAV multispectral images
orthophotos registration
3D information
orthophotos segmentation
wildfire detection
convolutional neural networks
densenet
generative adversarial networks
CycleGAN
data augmentation
pavement markings
visibility
framework
urban forests
OUDN algorithm
object-based
high spatial resolution remote sensing
Generative Adversarial Networks
post-disaster
building damage assessment
anomaly detection
Unmanned Aerial Vehicles (UAV)
xBD
feature engineering
orthophoto
unsupervised segmentation
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557747903321
Bazi Yakoub  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advances in Hyperspectral Data Exploitation
Advances in Hyperspectral Data Exploitation
Autore Chang Chein-I
Pubbl/distr/stampa Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 electronic resource (434 p.)
Soggetto topico Technology: general issues
History of engineering & technology
Soggetto non controllato hyperspectral image few-shot classification
deep learning
meta-learning
relation network
convolutional neural network
constrained-target optimal index factor band selection (CTOIFBS)
hyperspectral image
underwater spectral imaging system
underwater hyperspectral target detection
band selection (BS)
constrained energy minimization (CEM)
lightweight convolutional neural networks
hyperspectral imagery classification
transfer learning
air temperature
spatial measurement
FTIR
MWIR
carbon dioxide absorption
target detection
coffee beans
insect damage
hyperspectral imaging
band selection
visualization
color formation models
multispectral image
image fusion
joint tensor decomposition
anomaly detection
constrained sparse representation
hyperspectral imagery
moving target detection
spatio-temporal processing
hyperspectral remote sensing
image classification
constraint representation
superpixel segmentation
multiscale decision fusion
plug-and-play
denoising
nonlinear unmixing
spectral reconstruction
residual augmented attentional u-shape network
spatial augmented attention
channel augmented attention
boundary-aware constraint
atmospheric transmittance
temperature
emissivity
separation
midwave infrared
hyperspectral images
hyperspectral image super-resolution
data fusion
spectral-spatial residual network
self-supervised training
hyperspectral
vegetation
generative adversarial network
data augmentation
classification
rice leaf blast
hyperspectral imaging data
deep convolutional neural networks
fused features
evolutionary computation
heuristic algorithms
machine learning
unmanned aerial vehicles (UAVs)
vegetation mapping
upland swamps
mine environment
rice
rice leaf folder
hyperspectral image classification
change detection
self-supervised learning
attention mechanism
multi-source image fusion
SFIM
least square estimation
spatial filter
hyperspectral imaging (HSI)
hyperspectral target detection
hyperspectral reconstruction
hyperspectral unmixing
ISBN 3-0365-5796-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910637782203321
Chang Chein-I  
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Autore Kavzoglu Taskin
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (256 p.)
Soggetto topico Research & information: general
Soggetto non controllato 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
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557148403321
Kavzoglu Taskin  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Deep Learning Applications with Practical Measured Results in Electronics Industries
Deep Learning Applications with Practical Measured Results in Electronics Industries
Autore Kung Hsu-Yang
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (272 p.)
Soggetto non controllato faster region-based CNN
visual tracking
intelligent tire manufacturing
eye-tracking device
neural networks
A*
information measure
oral evaluation
GSA-BP
tire quality assessment
humidity sensor
rigid body kinematics
intelligent surveillance
residual networks
imaging confocal microscope
update mechanism
multiple linear regression
geometric errors correction
data partition
Imaging Confocal Microscope
image inpainting
lateral stage errors
dot grid target
K-means clustering
unsupervised learning
recommender system
underground mines
digital shearography
optimization techniques
saliency information
gated recurrent unit
multivariate time series forecasting
multivariate temporal convolutional network
foreign object
data fusion
update occasion
generative adversarial network
CNN
compressed sensing
background model
image compression
supervised learning
geometric errors
UAV
nonlinear optimization
reinforcement learning
convolutional network
neuro-fuzzy systems
deep learning
image restoration
neural audio caption
hyperspectral image classification
neighborhood noise reduction
GA
MCM uncertainty evaluation
binary classification
content reconstruction
kinematic modelling
long short-term memory
transfer learning
network layer contribution
instance segmentation
smart grid
unmanned aerial vehicle
forecasting
trajectory planning
discrete wavelet transform
machine learning
computational intelligence
tire bubble defects
offshore wind
multiple constraints
human computer interaction
Least Squares method
ISBN 3-03928-864-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910404080403321
Kung Hsu-Yang  
MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Hyperspectral Imaging and Applications
Hyperspectral Imaging and Applications
Autore Chang Chein-I
Pubbl/distr/stampa Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 electronic resource (632 p.)
Soggetto topico Technology: general issues
History of engineering & technology
Soggetto non controllato biodiversity
peatland
vegetation type
classification
hyperspectral
in situ measurements
hyperspectral image (HSI)
multiscale union regions adaptive sparse representation (MURASR)
multiscale spatial information
imaging spectroscopy
airborne laser scanning
minimum noise fraction
class imbalance
Africa
agroforestry
tree species
hyperspectral unmixing
endmember extraction
band selection
spectral variability
prototype space
ensemble learning
rotation forest
semi-supervised local discriminant analysis
optical spectral region
thermal infrared spectral region
mineral mapping
data integration
HyMap
AHS
raw material
remote sensing
nonnegative matrix factorization
data-guided constraints
sparseness
evenness
hashing ensemble
hierarchical feature
hyperspectral classification
band expansion process (BEP)
constrained energy minimization (CEM)
correlation band expansion process (CBEP)
iterative CEM (ICEM)
nonlinear band expansion (NBE)
Otsu’s method
sparse unmixing
local abundance
nuclear norm
hyperspectral detection
target detection
sprout detection
constrained energy minimization
iterative algorithm
adaptive window
hyperspectral imagery
recursive anomaly detection
local summation RX detector (LS-RXD)
sliding window
band selection (BS)
band subset selection (BSS)
hyperspectral image classification
linearly constrained minimum variance (LCMV)
successive LCMV-BSS (SC LCMV-BSS)
sequential LCMV-BSS (SQ LCMV-BSS)
vicarious calibration
reflectance-based method
irradiance-based method
Dunhuang site
90° yaw imaging
terrestrial hyperspectral imaging
vineyard
water stress
machine learning
tree-based ensemble
progressive sample processing (PSP)
real-time processing
image fusion
hyperspectral image
panchromatic image
structure tensor
image enhancement
weighted fusion
spectral mixture analysis
fire severity
AVIRIS
deep belief networks
deep learning
texture feature enhancement
band grouping
hyperspectral compression
lossy compression
on-board compression
orthogonal projections
Gram–Schmidt orthogonalization
parallel processing
anomaly detection
sparse coding
KSVD
hyperspectral images (HSIs)
SVM
composite kernel
algebraic multigrid methods
hyperspectral pansharpening
panchromatic
intrinsic image decomposition
weighted least squares filter
spectral-spatial classification
label propagation
superpixel
semi-supervised learning
rolling guidance filtering (RGF)
graph
deep pipelined background statistics
high-level synthesis
data fusion
data unmixing
hyperspectral imaging
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910585941603321
Chang Chein-I  
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Learning to Understand Remote Sensing Images . Volume 2
Learning to Understand Remote Sensing Images . Volume 2
Autore Wang Qi
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (363 pages)
Soggetto non controllato metadata
image classification
sensitivity analysis
ROI detection
residual learning
image alignment
adaptive convolutional kernels
Hough transform
class imbalance
land surface temperature
inundation mapping
multiscale representation
object-based
convolutional neural networks
scene classification
morphological profiles
hyperedge weight estimation
hyperparameter sparse representation
semantic segmentation
vehicle classification
flood
Landsat imagery
target detection
multi-sensor
building damage detection
optimized kernel minimum noise fraction (OKMNF)
sea-land segmentation
nonlinear classification
land use
SAR imagery
anti-noise transfer network
sub-pixel change detection
Radon transform
segmentation
remote sensing image retrieval
TensorFlow
convolutional neural network
particle swarm optimization
optical sensors
machine learning
mixed pixel
optical remotely sensed images
object-based image analysis
very high resolution images
single stream optimization
ship detection
ice concentration
online learning
manifold ranking
dictionary learning
urban surface water extraction
saliency detection
spatial attraction model (SAM)
quality assessment
Fuzzy-GA decision making system
land cover change
multi-view canonical correlation analysis ensemble
land cover
semantic labeling
sparse representation
dimensionality expansion
speckle filters
hyperspectral imagery
fully convolutional network
infrared image
Siamese neural network
Random Forests (RF)
feature matching
color matching
geostationary satellite remote sensing image
change feature analysis
road detection
deep learning
aerial images
image segmentation
aerial image
multi-sensor image matching
HJ-1A/B CCD
endmember extraction
high resolution
multi-scale clustering
heterogeneous domain adaptation
hard classification
regional land cover
hypergraph learning
automatic cluster number determination
dilated convolution
MSER
semi-supervised learning
gate
Synthetic Aperture Radar (SAR)
downscaling
conditional random fields
urban heat island
hyperspectral image
remote sensing image correction
skip connection
ISPRS
spatial distribution
geo-referencing
Support Vector Machine (SVM)
very high resolution (VHR) satellite image
classification
ensemble learning
synthetic aperture radar
conservation
convolutional neural network (CNN)
THEOS
visible light and infrared integrated camera
vehicle localization
structured sparsity
texture analysis
DSFATN
CNN
image registration
UAV
unsupervised classification
SVMs
SAR image
fuzzy neural network
dimensionality reduction
GeoEye-1
feature extraction
sub-pixel
energy distribution optimizing
saliency analysis
deep convolutional neural networks
sparse and low-rank graph
hyperspectral remote sensing
tensor low-rank approximation
optimal transport
SELF
spatiotemporal context learning
Modest AdaBoost
topic modelling
multi-seasonal
Segment-Tree Filtering
locality information
GF-4 PMS
image fusion
wavelet transform
hashing
machine learning techniques
satellite images
climate change
road segmentation
remote sensing
tensor sparse decomposition
Convolutional Neural Network (CNN)
multi-task learning
deep salient feature
speckle
canonical correlation weighted voting
fully convolutional network (FCN)
despeckling
multispectral imagery
ratio images
linear spectral unmixing
hyperspectral image classification
multispectral images
high resolution image
multi-objective
convolution neural network
transfer learning
1-dimensional (1-D)
threshold stability
Landsat
kernel method
phase congruency
subpixel mapping (SPM)
tensor
MODIS
GSHHG database
compressive sensing
ISBN 3-03897-699-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910367755503321
Wang Qi  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Learning to Understand Remote Sensing Images . Volume 1
Learning to Understand Remote Sensing Images . Volume 1
Autore Wang Qi
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (414 pages)
Soggetto non controllato metadata
image classification
sensitivity analysis
ROI detection
residual learning
image alignment
adaptive convolutional kernels
Hough transform
class imbalance
land surface temperature
inundation mapping
multiscale representation
object-based
convolutional neural networks
scene classification
morphological profiles
hyperedge weight estimation
hyperparameter sparse representation
semantic segmentation
vehicle classification
flood
Landsat imagery
target detection
multi-sensor
building damage detection
optimized kernel minimum noise fraction (OKMNF)
sea-land segmentation
nonlinear classification
land use
SAR imagery
anti-noise transfer network
sub-pixel change detection
Radon transform
segmentation
remote sensing image retrieval
TensorFlow
convolutional neural network
particle swarm optimization
optical sensors
machine learning
mixed pixel
optical remotely sensed images
object-based image analysis
very high resolution images
single stream optimization
ship detection
ice concentration
online learning
manifold ranking
dictionary learning
urban surface water extraction
saliency detection
spatial attraction model (SAM)
quality assessment
Fuzzy-GA decision making system
land cover change
multi-view canonical correlation analysis ensemble
land cover
semantic labeling
sparse representation
dimensionality expansion
speckle filters
hyperspectral imagery
fully convolutional network
infrared image
Siamese neural network
Random Forests (RF)
feature matching
color matching
geostationary satellite remote sensing image
change feature analysis
road detection
deep learning
aerial images
image segmentation
aerial image
multi-sensor image matching
HJ-1A/B CCD
endmember extraction
high resolution
multi-scale clustering
heterogeneous domain adaptation
hard classification
regional land cover
hypergraph learning
automatic cluster number determination
dilated convolution
MSER
semi-supervised learning
gate
Synthetic Aperture Radar (SAR)
downscaling
conditional random fields
urban heat island
hyperspectral image
remote sensing image correction
skip connection
ISPRS
spatial distribution
geo-referencing
Support Vector Machine (SVM)
very high resolution (VHR) satellite image
classification
ensemble learning
synthetic aperture radar
conservation
convolutional neural network (CNN)
THEOS
visible light and infrared integrated camera
vehicle localization
structured sparsity
texture analysis
DSFATN
CNN
image registration
UAV
unsupervised classification
SVMs
SAR image
fuzzy neural network
dimensionality reduction
GeoEye-1
feature extraction
sub-pixel
energy distribution optimizing
saliency analysis
deep convolutional neural networks
sparse and low-rank graph
hyperspectral remote sensing
tensor low-rank approximation
optimal transport
SELF
spatiotemporal context learning
Modest AdaBoost
topic modelling
multi-seasonal
Segment-Tree Filtering
locality information
GF-4 PMS
image fusion
wavelet transform
hashing
machine learning techniques
satellite images
climate change
road segmentation
remote sensing
tensor sparse decomposition
Convolutional Neural Network (CNN)
multi-task learning
deep salient feature
speckle
canonical correlation weighted voting
fully convolutional network (FCN)
despeckling
multispectral imagery
ratio images
linear spectral unmixing
hyperspectral image classification
multispectral images
high resolution image
multi-objective
convolution neural network
transfer learning
1-dimensional (1-D)
threshold stability
Landsat
kernel method
phase congruency
subpixel mapping (SPM)
tensor
MODIS
GSHHG database
compressive sensing
ISBN 3-03897-685-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910367755603321
Wang Qi  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui