TY - BOOK ID - 37123763 TI - Big Data for Remote Sensing: Visualization, Analysis and Interpretation : Digital Earth and Smart Earth AU - Dey, Nilanjan. AU - Bhatt, Chintan. AU - Ashour, Amira S. PY - 2019 SN - 3319899236 3319899228 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Remote sensing KW - Data processing. KW - Geographical information systems. KW - Computer vision. KW - Computer science KW - Geographical Information Systems/Cartography. KW - Environmental Science and Engineering. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Computational Mathematics and Numerical Analysis. KW - Mathematics. KW - Computer mathematics KW - Discrete mathematics KW - Electronic data processing KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Pattern recognition systems KW - Geographical information systems KW - GIS (Information systems) KW - Information storage and retrieval systems KW - Mathematics KW - Geography KW - Environmental sciences. KW - Optical data processing. KW - Computer mathematics. KW - Optical computing KW - Visual data processing KW - Bionics KW - Integrated optics KW - Photonics KW - Computers KW - Environmental science KW - Science KW - Optical equipment UR - https://www.unicat.be/uniCat?func=search&query=sysid:37123763 AB - This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. Remote sensing is considered a significant information source for monitoring and mapping natural and man-made land through the development of sensor resolutions that committed different Earth observation platforms. The book includes related topics for the different systems, models, and approaches used in the visualization of remote sensing images. It offers flexible and sophisticated solutions for removing uncertainty from the satellite data. It introduces real time big data analytics to derive intelligence systems in enterprise earth science applications. Furthermore, the book integrates statistical concepts with computer-based geographic information systems (GIS). It focuses on image processing techniques for observing data together with uncertainty information raised by spectral, spatial, and positional accuracy of GPS data. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes. Highlights on the advanced improvement models of the supervised/unsupervised classification algorithms, support vector machines, artificial neural networks, fuzzy logic, decision-making algorithms, and Time Series Model and Forecasting are addressed. This book guides engineers, designers, and researchers to exploit the intrinsic design remote sensing systems. The book gathers remarkable material from an international experts' panel to guide the readers during the development of earth big data analytics and their challenges. ER -