Reprint
Learning to Understand Remote Sensing Images
Volume 2
Edited by
September 2019
376 pages
- ISBN978-3-03897-698-1 (Paperback)
- ISBN978-3-03897-699-8 (PDF)
This book is a reprint of the Special Issue Learning to Understand Remote Sensing Images that was published in
This book is part of the book set Learning to Understand Remote Sensing Images
Engineering
Environmental & Earth Sciences
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.
Format
- Paperback
License
© 2019 by the authors; CC BY-NC-ND license
Keywords
hyperspectral image classification; SELF; SVMs; Segment-Tree Filtering; multi-sensor; change feature analysis; object-based; multispectral images; heterogeneous domain adaptation; transfer learning; multi-view canonical correlation analysis ensemble; semi-supervised learning; canonical correlation weighted voting; ensemble learning; image classification; spatial attraction model (SAM); subpixel mapping (SPM); land cover; mixed pixel; spatial distribution; hard classification; building damage detection; Fuzzy-GA decision making system; machine learning techniques; optical remotely sensed images; sensitivity analysis; texture analysis; quality assessment; ratio images; Synthetic Aperture Radar (SAR); speckle; speckle filters; ice concentration; SAR imagery; convolutional neural network; urban surface water extraction; threshold stability; sub-pixel; linear spectral unmixing; Landsat imagery; image registration; image fusion; UAV; metadata; visible light and infrared integrated camera; semantic segmentation; CNN; deep learning; ISPRS; remote sensing; gate; hyperspectral image; sparse and low-rank graph; tensor; dimensionality reduction; semantic labeling; convolution neural network; fully convolutional network; sea-land segmentation; ship detection; hyperspectral image; target detection; multi-task learning; sparse representation; locality information; remote sensing image correction; color matching; optimal transport; CNN; very high resolution images; segmentation; multi-scale clustering; vehicle localization; vehicle classification; high resolution; aerial image; convolutional neural network (CNN); class imbalance; deep learning; convolutional neural network (CNN); fully convolutional network (FCN); classification; remote sensing; high resolution; semantic segmentation; deep convolutional neural networks; manifold ranking; single stream optimization; high resolution image; feature extraction; hypergraph learning; morphological profiles; hyperedge weight estimation; semantic labeling; convolutional neural networks; remote sensing; deep learning; aerial images; hyperspectral image; feature extraction; dimensionality reduction; optimized kernel minimum noise fraction (OKMNF); hyperspectral remote sensing; endmember extraction; multi-objective; particle swarm optimization; image alignment; feature matching; geostationary satellite remote sensing image; GSHHG database; Hough transform; dictionary learning; road detection; Radon transform; geo-referencing; multi-sensor image matching; Siamese neural network; satellite images; synthetic aperture radar; inundation mapping; flood; optical sensors; spatiotemporal context learning; Modest AdaBoost; HJ-1A/B CCD; GF-4 PMS; hyperspectral image classification; automatic cluster number determination; adaptive convolutional kernels; hyperspectral imagery; 1-dimensional (1-D); Convolutional Neural Network (CNN); Support Vector Machine (SVM); Random Forests (RF); machine learning; deep learning; TensorFlow; multi-seasonal; regional land cover; saliency analysis; remote sensing; ROI detection; hyperparameter sparse representation; dictionary learning; energy distribution optimizing; multispectral imagery; nonlinear classification; kernel method; dimensionality expansion; deep convolutional neural networks; road segmentation; conditional random fields; satellite images; aerial images; THEOS; land cover change; downscaling; sub-pixel change detection; machine learning; MODIS; Landsat; very high resolution (VHR) satellite image; topic modelling; object-based image analysis; image segmentation; unsupervised classification; multiscale representation; GeoEye-1; wavelet transform; fuzzy neural network; remote sensing; conservation; urban heat island; land surface temperature; climate change; land use; land cover; Landsat; remote sensing; SAR image; despeckling; dilated convolution; skip connection; residual learning; scene classification; saliency detection; deep salient feature; anti-noise transfer network; DSFATN; infrared image; image registration; MSER; phase congruency; hashing; remote sensing image retrieval; online learning; hyperspectral image; compressive sensing; structured sparsity; tensor sparse decomposition; tensor low-rank approximation