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Learning to Understand Remote Sensing Images

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 remot...

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Bibliographic Details
Main Author:	Wang, Qi
Format:	eBook
Language:	English
Published:	MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:	Building Damage Detection Machine Learning Hypergraph Learning Gshhg Database Residual Learning Optical Sensors Sea-land Segmentation Fuzzy Neural Network Deep Convolutional Neural Networks Self Landsat Land Cover Change Land Cover Theos Kernel Method Fuzzy-ga Decision Making System Convolutional Neural Network (cnn) 1-dimensional (1-d) Multi-scale Clustering Metadata Quality Assessment Deep Learning Radon Transform Very High Resolution Images Spatiotemporal Context Learning Multi-objective Multispectral Images Color Matching Image Registration Siamese Neural Network Remote Sensing Image Correction Topic Modelling Texture Analysis Heterogeneous Domain Adaptation Road Segmentation Deep Salient Feature Target Detection Aerial Images Segment-tree Filtering Convolution Neural Network Sub-pixel Change Detection Image Fusion Feature Matching Saliency Detection Tensor Low-rank Approximation Road Detection Spatial Distribution Compressive Sensing Ship Detection Skip Connection Mixed Pixel Unsupervised Classification Speckle Hyperspectral Imagery Hyperspectral Image Canonical Correlation Weighted Voting Convolutional Neural Networks Uav Mser Segmentation Anti-noise Transfer Network Roi Detection Spatial Attraction Model (sam) Gate Urban Surface Water Extraction Semi-supervised Learning Adaptive Convolutional Kernels Morphological Profiles Image Classification Urban Heat Island Climate Change Tensor Sparse Decomposition Endmember Extraction Sar Image Optimal Transport Despeckling Geo-referencing Object-based Image Analysis Automatic Cluster Number Determination Conditional Random Fields Manifold Ranking Sparse And Low-rank Graph Modis Fully Convolutional Network (fcn) Particle Swarm Optimization Saliency Analysis Ratio Images Multiscale Representation Wavelet Transform Conservation Locality Information Hard Classification Dilated Convolution Multispectral Imagery Multi-view Canonical Correlation Analysis Ensemble Subpixel Mapping (spm) Dictionary Learning Multi-seasonal Satellite Images Change Feature Analysis Phase Congruency Visible Light And Infrared Integrated Camera Scene Classification Object-based Single Stream Optimization Hyperspectral Image Classification Geoeye-1 Synthetic Aperture Radar Optimized Kernel Minimum Noise Fraction (okmnf) Inundation Mapping High Resolution Image Remote Sensing Image Retrieval Optical Remotely Sensed Images Feature Extraction Landsat Imagery Hyperspectral Remote Sensing Ensemble Learning Synthetic Aperture Radar (sar) Energy Distribution Optimizing Image Alignment Transfer Learning Vehicle Localization Vehicle Classification Multi-sensor High Resolution Aerial Image Isprs Regional Land Cover Online Learning Dimensionality Reduction Modest Adaboost Support Vector Machine (svm) Downscaling Random Forests (rf) Sensitivity Analysis Dsfatn Ice Concentration Svms Hyperparameter Sparse Representation Gf-4 Pms Speckle Filters Geostationary Satellite Remote Sensing Image Machine Learning Techniques Flood Remote Sensing Semantic Labeling Cnn Land Surface Temperature Hough Transform Nonlinear Classification Sparse Representation Sar Imagery Classification Threshold Stability Structured Sparsity Semantic Segmentation Hashing Sub-pixel Multi-sensor Image Matching Very High Resolution (vhr) Satellite Image Hj-1a/b Ccd Convolutional Neural Network Hyperedge Weight Estimation Infrared Image Dimensionality Expansion Class Imbalance Land Use Tensorflow Fully Convolutional Network Linear Spectral Unmixing Image Segmentation Multi-task Learning Tensor
Online Access:	https://directory.doabooks.org/handle/20.500.12854... https://www.mdpi.com/books/pdfview/book/1632
Collection:	Directory of Open Access Books - Collection details see MPG.ReNa

Description
Summary:	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.
Item Description:	Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
Physical Description:	1 electronic resource (376 p.)
ISBN:	9783038976998 9783038976981 books978-3-03897-699-8

Learning to Understand Remote Sensing Images

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