Learning to Understand Remote Sensing Images . Volume 2

• Autore: Wang Qi
• Pubblicazione: 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
• Sommario/riassunto: 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.
• Titolo autorizzato: Learning to Understand Remote Sensing Images
• ISBN: 3-03897-699-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910367755503321
• Lo trovi qui: Univ. Federico II