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Full Description
This book is a composition of diverse points of view regarding the application of Computational Intelligence techniques and methods into Remote Sensing data and problems. It is the general consensus that classi?cation, and related data processing, and global optimization methods are the main topics of Compu- tional Intelligence. Global random optimization algorithms appear in this book, such as the Simulated Annealing in chapter 6 and the Genetic Algorithms p- posedinchapters3and9. Muchofthecontentsofthe bookaredevotedto image segmentationandrecognition,using diversetoolsfromregionsofComputational Intelligence, ranging from Arti?cial Neural Networks to Markov Random Field modelling. However, there are some fringe topics, such the parallel implem- tation of some algorithms or the image watermarking that make evident that thefrontiersbetweenComputationalIntelligenceandneighboringcomputational disciplines are blurred and the fences run low and full of holes in many places. The book starts with a review of the current designs of hyperspectral sensors, more appropriately named Imaging Spectrometers.
Knowing the shortcomings and advantages of the diverse designs may condition the results on some app- cations of Computational Intelligence algorithms to the processing and und- standing of them Remote Sensing images produced by these sensors. Then the book contentsmovesinto basic signalprocessing techniquessuch ascompression and watermarking applied to remote sensing images. With the huge amount of remotesensinginformationandtheincreasingrateatwhichitisbeingproduced, itseems only naturalthatcompressiontechniques willleapintoa prominentrole in the near future, overcoming the resistances of the users against uncontrolled manipulation of "their" data. Watermarkingis the way to address issues of o- ership authentication in digital contents.
Contents
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.
