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Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest...

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Bibliographic Details
Main Author:	Akhloufi, Moulay A.
Other Authors:	Shahbazi, Mozhdeh
Format:	eBook
Language:	English
Published:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:	Temperature Field Machine Learning Off-grid Natural Hazard Optical Sensors Remote Sensing Sensors U-net Geoinformatics Extreme Events Outdated Building Map Vhr Images Fire Classification Fusion Deep Learning Very High-resolution Super-resolution Geometry Structure Agb Alternating Decision Trees Spatial Model Wildfire Detection Network Deep Learning Neural Network Technology: General Issues / Bicssc Target Detection Aerial Images Attention Mechanism Remote Sensing Images Convolutional Networks Uav Prediction Chimney Meteorological Parameters Change Detection Nsfs Faster R-cnn Particle Swarm Optimization Space-frequency Pseudo-spectrum Radar Modulation Signal High Spatial Resolution Images Typhoon Bivariate Statistics Hazard Map Pso Unmanned Aerial Vehicle (uav) Object-based History Of Engineering And Technology / Bicssc Rural Settlements Thermophysical Parameters Full Convolutional Network Fire Segmentation High Resolution Remote Sensing Image Image Enhancement Mask R-cnn Scattered Vegetation Geohazard Multi-scale Context Deep Neural Networks Circularly Fully Convolutional Networks Disease Classification Feature Extraction Ensemble Learning Doa Estimation Time-frequency Analysis High Resolution Deep-learning Complex Morlet Wavelet Power Transmission Lines Cross-layer Feature Fusion Ensemble Models Environmental Science, Engineering And Technology / Bicssc Fully Convolutional Feature Maps Flash-flood Potential Index Dlnn Remote Sensing Vision Transformers Convolutional Neural Networks Erosion Generative Adversarial Networks Channel-separable Resnet Changes Detection Rainfall Gully Erosion Susceptibility Spatial Analysis Three-dimensional Scene Intelligent Prediction Cultivated Land Extraction Vibration Dampers Detection Ensemble Model Fully Convolutional Network Image Segmentation
Online Access:	https://www.mdpi.com/books/pdfview/book/6279 https://directory.doabooks.org/handle/20.500.12854...
Collection:	Directory of Open Access Books - Collection details see MPG.ReNa

Description
Summary:	Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing.
Item Description:	Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:	1 electronic resource (344 p.)
ISBN:	9783036546308 books978-3-0365-4630-8 9783036546292

Deep Learning Methods for Remote Sensing

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