Artificial Intelligence Techniques for Satellite Image Analysis
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The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.
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Jude Hemanth received his BE degree in ECE from Bharathiar University in 2002, his ME in Communication Systems from Anna University in 2006, and his PhD from Karunya University in 2013. His main research areas include computational intelligence and image processing. He has authored more than 100 research papers in reputed International Journals.
He has authored one book and numerous book chapters with reputed publishers such as Springer and Inderscience. He has served as Associate Editor of IEEE Access Journal, and is currently a Guest Editor for many journals published by Springer, Inderscience, IOS Press, etc. A member of the IEEE Technical Committee on Neural Networks (IEEE Computational Intelligence Society) and IEEE Technical Committee on Soft Computing (IEEE Systems, Man and Cybernetics Society), Prof Hemanth has completed one funded research project for the CSIR, Government of India. He is also a Research Scientist at the Computational Intelligence and Information Systems (CI2S) Lab, Argentina and RIADI Lab, Tunisia. Currently, he is working as an Associate Professor at the Department of ECE, Karunya University, India.Content
1. Heightening satellite image display via mobile augmented reality - a cutting-edge planning model.- 2. Multithreading Approach for clustering of Multi-Plane Satellite Images.- 3. Classification of field level crop types with a time series satellite data using Deep Neural Network.- 4. Detection of ship from satellite images using deep convolutional neural networks with improved median filter.- 5. Artificial Bee Colony optimized contrast enhancement for satellite image fusion.- 6. Effective transform domain denoising of oceanographic sar images for improved target characterization.- 7. Fused segmentation algorithm for the detection of nutrient deficiency in crops using SAR images.- 8. Detection of natural features and objects in satellite images by semantic segmentation using neural networks.- 9. Change Detection of Tropical Mangrove Ecosystem with Subpixel Classification of Time Series Hyperspectral Imagery.- 10. Crop Classification and Mapping for Agricultural Land from Satellite Images.- 11. Next Generation Artificial Intelligence Techniques for Satellite Data Processing.- 12. A wavelet transform applied spectral index for effective water body extraction from moderate resolution satellite images.