Remote Sensing In Geoscience
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Remote Sensing Geology
Author: Ravi P. Gupta
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
For nearly three decades there has been a phenomenal growth in the field of Remote Sensing. The second edition of this widely acclaimed book has been fully revised and updated. The reader will find a wide range of information on various aspects of geological remote sensing, ranging from laboratory spectra of minerals and rocks, ground truth, to aerial and space-borne remote sensing. This volume describes the integration of photogeology into remote sensing as well as how remote sensing is used as a tool of geo-exploration. It also covers a wide spectrum of geoscientific applications of remote sensing ranging from meso- to global scale. The subject matter is presented at a basic level, serving students as an introductory text on remote sensing. The main part of the book will also be of great value to active researchers.
Remote Sensing in Geoscience
Author: Nitin Kumar Tripathi
language: en
Publisher: Anmol Publications Pvt Limited
Release Date: 1998
Remote Sensing Technology Is Today Widely Used In The Survey And Management Of Natural Resources. The Present Book Contains Much Informative And Well-Researched Articles, Contributed By Eminent Academics And Scientists In The Field. Topics Like Ground Water Prospecting Through Remote Sensing; Data Integration In Geographic Information System And Its Efficacy; Highlights Of Advanced Technologies In Remote Sensing; Utility Of Gis And Image Processing Methods For Agro-Based Commercial Ventures; Remote Sensing Methodologies For Indian Small Scale Mining Industry; Filtering Applications In Geosciences; Integration Of Collateral Data With Remote Sensing Data; Ground Water Budgeting Through Satellite Data; Neural Network And Fuzzy Logic In Remote Sensing; Image Enhancement Analysis For Improving Classification Accuracy Over Vegetated Areas; Evidence Classifier For Land Use/Land Cover Classification; Morphological Image Processing; Integration Of Spot And Sar Images For Monitoring Of Environmental Changes By A Fuzzy Neural Network; Correlation Of Landsat Images To Resistivity And Seismic Structures In A Sedimentary Basin; Novel Vegetation Indices For Remote Sensing Of Chlorophyll Contents In Higher Plants; Remote Sensing Of Chlorophyll; Evaluation Of Groundwater Potentials In Hard Rock Terrains Through Geomorphic Mapping; Application Of Remote Sensing To Land Slide Studies; Use Of Remotely Sensed Data To Study And Arid Terrain, Wadi Tabalah Area, Kingdom Of Saudi Arabia; Application Of Remote Sensing Technology In Mapping And Monitoring Salt Affected Soils; Lineament Mapping On Satellite Images For Deciphering Hydrogeologic Situation In Banas River Basin, Etc. Researchers, Scientists And Academics Will Find This Book Of Utmost Use.
Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls
language: en
Publisher: John Wiley & Sons
Release Date: 2021-08-18
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.