Computational Intelligence In Remote Sensing
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Computational Intelligence in Remote Sensing
With the advancement of Earth observation techniques, vast amounts of high-resolution remote sensing data are continually captured, proving instrumental in fields such as geography, environmental monitoring, disaster management, and more. However, challenges such as data volume, complex structures, limited labeled samples, and non-convex optimization persist in processing and analyzing remote sensing data. Computational intelligence techniques, inspired by biological intelligence systems, offer potential solutions to these challenges. Computational intelligence (CI) is the theory, design, and application of biologically and linguistically motivated computational paradigms. Traditionally centered around neural networks, fuzzy systems, and evolutionary computation, CI has expanded to include various nature-inspired computing paradigms. These paradigms encompass ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a vital role in developing intelligent systems, including games and cognitive developmental systems. Recent years have seen a surge in deep learning research, with deep convolutional neural networks becoming a core method in artificial intelligence. Many successful AI systems today are based on CI, and it is anticipated that CI will provide effective solutions to challenges in remote sensing in the future.
Computational Intelligence in Remote Sensing
With the advancement of Earth observation techniques, vast amounts of high-resolution remote sensing data are continually captured, proving instrumental in fields such as geography, environmental monitoring, disaster management, and more. However, challenges such as data volume, complex structures, limited labeled samples, and non-convex optimization persist in processing and analyzing remote sensing data. Computational intelligence techniques, inspired by biological intelligence systems, offer potential solutions to these challenges. Computational intelligence (CI) is the theory, design, and application of biologically and linguistically motivated computational paradigms. Traditionally centered around neural networks, fuzzy systems, and evolutionary computation, CI has expanded to include various nature-inspired computing paradigms. These paradigms encompass ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a vital role in developing intelligent systems, including games and cognitive developmental systems. Recent years have seen a surge in deep learning research, with deep convolutional neural networks becoming a core method in artificial intelligence. Many successful AI systems today are based on CI, and it is anticipated that CI will provide effective solutions to challenges in remote sensing in the future.
Computational Intelligence for Remote Sensing
Author: Manuel Grana
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-06-05
This book is a composition of different points of view regarding the application of Computational Intelligence techniques and methods to Remote Sensing data and applications. It is the general consensus that classification, its related data processing, and global optimization methods are core topics of Computational Intelligence. Much of the content of the book is devoted to image segmentation and recognition, using diverse tools from different areas of the Computational Intelligence field, ranging from Artificial Neural Networks to Markov Random Field modeling. The book covers a broad range of topics, starting from the hardware design of hyperspectral sensors, and data handling problems, namely data compression and watermarking issues, as well as autonomous web services. The main contents of the book are devoted to image analysis and efficient (parallel) implementations of these analysis techniques. The classes of images dealt with throughout the book are mostly multispectral-hyperspectral images, though there are some instances of processing Synthetic Aperture Radar images.