TY - GEN T1 - Learning to Understand Remote Sensing Images A1 - Wang, Qi LA - eng PB - MDPI - Multidisciplinary Digital Publishing Institute YR - 2021 UL - https://ebooks.jgu.edu.in/Record/doab-20.500.12854-51488 AB - 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. SN - 9783038976851 SN - 9783038976844 KW - QA75.5-76.95 KW - T58.5-58.64 KW - metadata KW - image classification KW - sensitivity analysis KW - ROI detection KW - residual learning KW - image alignment KW - adaptive convolutional kernels KW - Hough transform KW - class imbalance KW - land surface temperature KW - inundation mapping KW - multiscale representation KW - object-based KW - convolutional neural networks KW - scene classification KW - morphological profiles KW - hyperedge weight estimation KW - hyperparameter sparse representation KW - semantic segmentation KW - vehicle classification KW - flood KW - Landsat imagery KW - target detection KW - multi-sensor KW - building damage detection KW - optimized kernel minimum noise fraction (OKMNF) KW - sea-land segmentation KW - nonlinear classification KW - land use KW - SAR imagery KW - anti-noise transfer network KW - sub-pixel change detection KW - Radon transform KW - segmentation KW - remote sensing image retrieval KW - TensorFlow KW - convolutional neural network KW - particle swarm optimization KW - optical sensors KW - machine learning KW - mixed pixel KW - optical remotely sensed images KW - object-based image analysis KW - very high resolution images KW - single stream optimization KW - ship detection KW - ice concentration KW - online learning KW - manifold ranking KW - dictionary learning KW - urban surface water extraction KW - saliency detection KW - spatial attraction model (SAM) KW - quality assessment KW - Fuzzy-GA decision making system KW - land cover change KW - multi-view canonical correlation analysis ensemble KW - land cover KW - semantic labeling KW - sparse representation KW - dimensionality expansion KW - speckle filters KW - hyperspectral imagery KW - fully convolutional network KW - infrared image KW - Siamese neural network KW - Random Forests (RF) KW - feature matching KW - color matching KW - geostationary satellite remote sensing image KW - change feature analysis KW - road detection KW - deep learning KW - aerial images KW - image segmentation KW - aerial image KW - multi-sensor image matching KW - HJ-1A/B CCD KW - endmember extraction KW - high resolution KW - multi-scale clustering KW - heterogeneous domain adaptation KW - hard classification KW - regional land cover KW - hypergraph learning KW - automatic cluster number determination KW - dilated convolution KW - MSER KW - semi-supervised learning KW - gate KW - Synthetic Aperture Radar (SAR) KW - downscaling KW - conditional random fields KW - urban heat island KW - hyperspectral image KW - remote sensing image correction KW - skip connection KW - ISPRS KW - spatial distribution KW - geo-referencing KW - Support Vector Machine (SVM) KW - very high resolution (VHR) satellite image KW - classification KW - ensemble learning KW - synthetic aperture radar KW - conservation KW - convolutional neural network (CNN) KW - THEOS KW - visible light and infrared integrated camera KW - vehicle localization KW - structured sparsity KW - texture analysis KW - DSFATN KW - CNN KW - image registration KW - UAV KW - unsupervised classification KW - SVMs KW - SAR image KW - fuzzy neural network KW - dimensionality reduction KW - GeoEye-1 KW - feature extraction KW - sub-pixel KW - energy distribution optimizing KW - saliency analysis KW - deep convolutional neural networks KW - sparse and low-rank graph KW - hyperspectral remote sensing KW - tensor low-rank approximation KW - optimal transport KW - SELF KW - spatiotemporal context learning KW - Modest AdaBoost KW - topic modelling KW - multi-seasonal KW - Segment-Tree Filtering KW - locality information KW - GF-4 PMS KW - image fusion KW - wavelet transform KW - hashing KW - machine learning techniques KW - satellite images KW - climate change KW - road segmentation KW - remote sensing KW - tensor sparse decomposition KW - Convolutional Neural Network (CNN) KW - multi-task learning KW - deep salient feature KW - speckle KW - canonical correlation weighted voting KW - fully convolutional network (FCN) KW - despeckling KW - multispectral imagery KW - ratio images KW - linear spectral unmixing KW - hyperspectral image classification KW - multispectral images KW - high resolution image KW - multi-objective KW - convolution neural network KW - transfer learning KW - 1-dimensional (1-D) KW - threshold stability KW - Landsat KW - kernel method KW - phase congruency KW - subpixel mapping (SPM) KW - tensor KW - MODIS KW - GSHHG database KW - compressive sensing KW - thema EDItEUR::U Computing and Information Technology::UY Computer science ER -