http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Computational Intelligence for Remote Sensing
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.
Optical Configurations for Imaging Spectrometers -- Remote Sensing Data Compression -- A Multiobjective Evolutionary Algorithm for Hyperspectral Image Watermarking -- Architecture and Services for Computational Intelligence in Remote Sensing -- On Content-Based Image Retrieval Systems for Hyperspectral Remote Sensing Images -- An Analytical Approach to the Optimal Deployment of Wireless Sensor Networks -- Parallel Spatial-Spectral Processing of Hyperspectral Images -- Parallel Classification of Hyperspectral Images Using Neural Networks -- Positioning Weather Systems from Remote Sensing Data Using Genetic Algorithms -- A Computation Reduced Technique to Primitive Feature Extraction for Image Information Mining Via the Use of Wavelets -- Neural Networks for Land Cover Applications -- Information Extraction for Forest Fires Management -- Automatic Preprocessing and Classification System for High Resolution Ultra and Hyperspectral Images -- Using Gaussian Synapse ANNs for Hyperspectral Image Segmentation and Endmember Extraction -- Unsupervised Change Detection from Multichannel SAR Data by Markov Random Fields.
edited by Manuel Graña, Richard J. Duro.
Appl.Mathematics/Computational Methods of Engineering
Artificial Intelligence (incl. Robotics)
Studies in Computational Intelligence, 133