Generative Adversarial Networks For Remote Sensing
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Generative Adversarial Networks for Remote Sensing
Author: Vibhute, Amol Dattatraya
language: en
Publisher: IGI Global
Release Date: 2025-04-30
Generative adversarial networks (GANs) are transforming the way complex remote sensing data is analyzed, offering innovative solutions for geospatial applications. Traditional methods often struggle to process high-dimensional remotely sensed datasets, leading to limitations in decision-making and predictive accuracy. By leveraging GANs, researchers can enhance feature extraction, object detection, and time-series analysis, enabling more precise environmental monitoring, urban planning, and agricultural assessments. This technological advancement not only improves real-time geospatial analysis but also opens new avenues for interdisciplinary collaboration, ethical considerations, and security challenges in AI-driven remote sensing. As GANs continue to evolve, their application in remote sensing holds the potential to drive sustainability and more informed global decision-making. Generative Adversarial Networks for Remote Sensing emphasizes the foundations of recent trends in GANs and remote sensing applications. It provides insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analyzing remote sensing data using GANs. Covering topics such as change detection, resource management, and feature engineering, this book is an excellent resource for geographers, geospatial data analysts, engineers, professionals, researchers, scholars, academicians, and more.
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.
2018 25th IEEE International Conference on Image Processing (ICIP)
The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing ICIP 2018, the 25th in the series that has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world