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210512 ||| eng |
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|a 9783038976844
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|a 9783038976851
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|a books978-3-03897-685-1
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100 |
1 |
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|a Wang, Qi
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245 |
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|a Learning to Understand Remote Sensing Images
|h Elektronische Ressource
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260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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300 |
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|a 1 electronic resource (426 p.)
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|a building damage detection
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|a machine learning
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|a hypergraph learning
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|a GSHHG database
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|a residual learning
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|a optical sensors
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|a sea-land segmentation
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|a fuzzy neural network
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|a deep convolutional neural networks
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|a SELF
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|a Landsat
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|a land cover change
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|a land cover
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|a THEOS
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|a kernel method
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|a Fuzzy-GA decision making system
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|a Convolutional Neural Network (CNN)
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|a 1-dimensional (1-D)
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|a multi-scale clustering
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|a metadata
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|a quality assessment
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|a deep learning
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|a Radon transform
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|a very high resolution images
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|a spatiotemporal context learning
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|a multi-objective
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|a multispectral images
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|a color matching
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|a image registration
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|a Siamese neural network
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|a remote sensing image correction
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|a topic modelling
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|a texture analysis
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|a heterogeneous domain adaptation
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|a road segmentation
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|a deep salient feature
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|a target detection
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|a aerial images
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|a Segment-Tree Filtering
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|a convolution neural network
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|a sub-pixel change detection
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|a image fusion
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|a feature matching
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|a saliency detection
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|a tensor low-rank approximation
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|a road detection
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|a spatial distribution
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|a compressive sensing
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|a ship detection
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|a skip connection
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|a mixed pixel
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|a unsupervised classification
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|a speckle
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|a hyperspectral imagery
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|a hyperspectral image
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|a canonical correlation weighted voting
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|a convolutional neural networks
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|a UAV
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|a MSER
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|a segmentation
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|a anti-noise transfer network
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|a ROI detection
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|a spatial attraction model (SAM)
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|a gate
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|a urban surface water extraction
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|a semi-supervised learning
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|a adaptive convolutional kernels
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|a morphological profiles
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|a image classification
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|a urban heat island
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|a climate change
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|a tensor sparse decomposition
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|a endmember extraction
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|a SAR image
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|a optimal transport
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|a despeckling
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|a geo-referencing
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|a object-based image analysis
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|a automatic cluster number determination
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|a conditional random fields
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|a manifold ranking
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|a sparse and low-rank graph
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|a MODIS
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|a fully convolutional network (FCN)
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|a particle swarm optimization
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|a saliency analysis
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|a ratio images
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|a multiscale representation
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|a wavelet transform
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|a conservation
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|a locality information
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|a hard classification
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|a dilated convolution
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|a multispectral imagery
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|a multi-view canonical correlation analysis ensemble
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|a subpixel mapping (SPM)
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|a dictionary learning
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|a multi-seasonal
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|a satellite images
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|a change feature analysis
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|a phase congruency
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|a visible light and infrared integrated camera
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|a scene classification
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|a object-based
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|a single stream optimization
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|a hyperspectral image classification
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|a GeoEye-1
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|a optimized kernel minimum noise fraction (OKMNF)
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|a inundation mapping
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|a synthetic aperture radar
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|a high resolution image
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|a remote sensing image retrieval
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|a optical remotely sensed images
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|a feature extraction
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|a Landsat imagery
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|a hyperspectral remote sensing
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|a Synthetic Aperture Radar (SAR)
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|a ensemble learning
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|a energy distribution optimizing
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|a image alignment
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|a transfer learning
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|a vehicle localization
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|a vehicle classification
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|a multi-sensor
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|a high resolution
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|a aerial image
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|a ISPRS
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|a regional land cover
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|a online learning
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|a dimensionality reduction
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|a Modest AdaBoost
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|a Support Vector Machine (SVM)
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|a Computer science / bicssc
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|a downscaling
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|a Random Forests (RF)
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|a sensitivity analysis
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|a DSFATN
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|a ice concentration
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|a SVMs
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|a hyperparameter sparse representation
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|a GF-4 PMS
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|a speckle filters
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|a geostationary satellite remote sensing image
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|a machine learning techniques
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|a flood
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|a remote sensing
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|a semantic labeling
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653 |
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|a CNN
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653 |
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|a Hough transform
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653 |
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|a land surface temperature
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653 |
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|a nonlinear classification
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653 |
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|a sparse representation
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653 |
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|a SAR imagery
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653 |
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|a classification
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653 |
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|a threshold stability
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653 |
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|a structured sparsity
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653 |
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|a semantic segmentation
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653 |
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|a convolutional neural network (CNN)
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653 |
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|a hashing
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653 |
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|a sub-pixel
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653 |
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|a multi-sensor image matching
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653 |
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|a very high resolution (VHR) satellite image
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653 |
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|a HJ-1A/B CCD
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653 |
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|a convolutional neural network
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653 |
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|a hyperedge weight estimation
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|a infrared image
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|a dimensionality expansion
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|a class imbalance
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|a land use
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653 |
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|a TensorFlow
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|a fully convolutional network
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|a linear spectral unmixing
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|a image segmentation
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|a multi-task learning
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|a tensor
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041 |
0 |
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|a eng
|2 ISO 639-2
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989 |
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|b DOAB
|a Directory of Open Access Books
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500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
|
028 |
5 |
0 |
|a 10.3390/books978-3-03897-685-1
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856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/1629
|7 0
|x Verlag
|3 Volltext
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856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/51488
|z DOAB: description of the publication
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|a 000
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|a 551.6
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|a 333
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|a 320
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|a 380
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|a With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
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