Learning to Understand Remote Sensing Images

Volume 2

Qi Wang (Herausgeber)

Buch | Softcover

376 Seiten

2019
MDPI (Verlag)
978-3-03897-698-1 (ISBN)

Infos

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.

Produkt-Details

• Erscheint lt. Verlag: 30.9.2019
• Verlagsort: Basel
• Sprache: englisch
• Maße: 170 x 244 mm
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: 1-dimensional (1-D) • adaptive convolutional kernels • aerial image • Aerial Images • anti-noise transfer network • automatic cluster number determination • building damage detection • canonical correlation weighted voting • change feature analysis • classification • Class Imbalance • climate change • CNN • Color matching • compressive sensing • Conditional Random Fields • conservation • convolutional neural network • convolutional neural network (CNN) • convolutional neural networks • Convolution Neural Network • deep convolutional neural networks • Deep learning • deep salient feature • Despeckling • Dictionary Learning • dilated convolution • dimensionality expansion • dimensionality reduction • Downscaling • DSFATN • Endmember Extraction • energy distribution optimizing • ensemble learning • feature extraction • feature matching • Flood • fully convolutional network • fully convolutional network (FCN) • Fuzzy-GA decision making system • fuzzy neural network • gate • GeoEye-1 • geo-referencing • geostationary satellite remote sensing image • GF-4 PMS • GSHHG database • hard classification • Hashing • heterogeneous domain adaptation • high resolution • high resolution image • HJ-1A/B CCD • Hough Transform • hyperedge weight estimation • hypergraph learning • hyperparameter sparse representation • Hyperspectral Image • Hyperspectral image classification • Hyperspectral Imagery • Hyperspectral Remote Sensing • ice concentration • image alignment • image classification • image fusion • Image Registration • Image Segmentation • infrared image • Inundation Mapping • ISPRS • Kernel Method • Land cover • Land cover change • Landsat • Landsat imagery • Land Surface Temperature • land use • linear spectral unmixing • locality information • machine learning • machine learning techniques • manifold ranking • Metadata • Mixed pixel • Modest AdaBoost • MODIS • morphological profiles • MSER • Multi-objective • multi-scale clustering • Multiscale Representation • multi-seasonal • multi-sensor • multi-sensor image matching • multispectral imagery • multispectral images • multi-task learning • multi-view canonical correlation analysis ensemble • nonlinear classification • Object-based • object-based image analysis • Online Learning • optical remotely sensed images • Optical sensors • optimal transport • optimized kernel minimum noise fraction (OKMNF) • Particle swarm optimization • phase congruency • Quality Assessment • Radon Transform • Random Forests (RF) • ratio images • regional land cover • Remote Sensing • remote sensing image correction • remote sensing image retrieval • residual learning • Road Detection • road segmentation • ROI detection • saliency analysis • saliency detection • SAR image • SAR imagery • Satellite images • scene classification • sea-land segmentation • Segmentation • Segment-Tree Filtering • self • Semantic labeling • semantic segmentation • Semi-Supervised Learning • Sensitivity Analysis • ship detection • Siamese neural network • single stream optimization • skip connection • sparse and low-rank graph • Sparse Representation • spatial attraction model (SAM) • Spatial distribution • spatiotemporal context learning • speckle • speckle filters • Structured Sparsity • sub-pixel • sub-pixel change detection • subpixel mapping (SPM) • Support Vector Machine (SVM) • SVMs • Synthetic Aperture Radar • Synthetic aperture radar (SAR) • target detection • Tensor • tensorflow • tensor low-rank approximation • tensor sparse decomposition • Texture Analysis • Theos • threshold stability • Topic modelling • transfer learning • UAV • unsupervised classification • Urban Heat Island • urban surface water extraction • Vehicle Classification • vehicle localization • very high resolution images • very high resolution (VHR) satellite image • visible light and infrared integrated camera • wavelet transform
• ISBN-10: 3-03897-698-9 / 3038976989
• ISBN-13: 978-3-03897-698-1 / 9783038976981
• Zustand: Neuware