Computational Intelligence for Remote Sensing (Studies in Computational Intelligence, Volume 133)

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<ul><li><p>Manuel Grana and Richard J. Duro (Eds.)</p><p>Computational Intelligence for Remote Sensing</p></li><li><p>Studies in Computational Intelligence,Volume 133</p><p>Editor-in-ChiefProf. Janusz KacprzykSystems Research InstitutePolish Academy of Sciencesul. Newelska 601-447WarsawPolandE-mail:</p><p>Further volumes of this series can be found on our</p><p>Vol. 113. Gemma Bel-Enguix,M. Dolores Jimenez-Lopezand Carlos Martn-Vide (Eds.)New Developments in Formal Languages and Applications, 2008ISBN 978-3-540-78290-2</p><p>Vol. 114. Christian Blum,Maria Jose Blesa Aguilera,Andrea Roliand Michael Sampels (Eds.)Hybrid Metaheuristics, 2008ISBN 978-3-540-78294-0</p><p>Vol. 115. John Fulcher and Lakhmi C. Jain (Eds.)Computational Intelligence: A Compendium, 2008ISBN 978-3-540-78292-6</p><p>Vol. 116.Ying Liu,Aixin Sun, Han Tong Loh,Wen Feng Luand Ee-Peng Lim (Eds.)Advances of Computational Intelligence in Industrial Systems,2008ISBN 978-3-540-78296-4</p><p>Vol. 117. Da Ruan, Frank Hardemanand Klaas van der Meer (Eds.)Intelligent Decision and Policy Making Support Systems, 2008ISBN 978-3-540-78306-0</p><p>Vol. 118. Tsau Young Lin,Ying Xie,AnitaWasilewskaand Churn-Jung Liau (Eds.)Data Mining: Foundations and Practice, 2008ISBN 978-3-540-78487-6</p><p>Vol. 119. SlawomirWiak,Andrzej Krawczyk andIvo Dolezel (Eds.)Intelligent Computer Techniques in Applied Electromagnetics,2008ISBN 978-3-540-78489-0</p><p>Vol. 120. George A. Tsihrintzis and Lakhmi C. Jain (Eds.)Multimedia Interactive Services in Intelligent Environments,2008ISBN 978-3-540-78491-3</p><p>Vol. 121. Nadia Nedjah, Leandro dos Santos Coelhoand Luiza de Macedo Mourelle (Eds.)Quantum Inspired Intelligent Systems, 2008ISBN 978-3-540-78531-6</p><p>Vol. 122. Tomasz G. Smolinski,Mariofanna G.Milanovaand Aboul-Ella Hassanien (Eds.)Applications of Computational Intelligence in Biology, 2008ISBN 978-3-540-78533-0</p><p>Vol. 123. Shuichi Iwata,Yukio Ohsawa, Shusaku Tsumoto, NingZhong,Yong Shi and Lorenzo Magnani (Eds.)Communications and Discoveries from MultidisciplinaryData,2008ISBN 978-3-540-78732-7</p><p>Vol. 124. Ricardo Zavala YoeModelling and Control of Dynamical Systems: NumericalImplementation in a Behavioral Framework, 2008ISBN 978-3-540-78734-1</p><p>Vol. 125. Larry Bull, Bernado-Mansilla Esterand John Holmes (Eds.)Learning Classifier Systems in Data Mining,2008ISBN 978-3-540-78978-9</p><p>Vol. 126. Oleg Okun and GiorgioValentini (Eds.)Supervised and Unsupervised Ensemble Methodsand their Applications, 2008ISBN 978-3-540-78980-2</p><p>Vol. 127. Regie Gras, Einoshin Suzuki, Fabrice Guilletand Filippo Spagnolo (Eds.)Statistical Implicative Analysis, 2008ISBN 978-3-540-78982-6</p><p>Vol. 128. Fatos Xhafa and Ajith Abraham (Eds.)Metaheuristics for Scheduling in Industrial and ManufacturingApplications, 2008ISBN 978-3-540-78984-0</p><p>Vol. 129. Natalio Krasnogor, Giuseppe Nicosia,Mario Pavoneand David Pelta (Eds.)Nature Inspired Cooperative Strategies for Optimization (NICSO2007), 2008ISBN 978-3-540-78986-4</p><p>Vol. 130. Richi Nayak, Nikhil Ichalkaranjeand Lakhmi C. Jain (Eds.)Evolution of the Web in Artificial Intelligence Environments,2008ISBN 978-3-540-79140-9</p><p>Vol. 131. Roger Lee and Haeng-Kon Kim (Eds.)Computer and Information Science, 2008ISBN 978-3-540-79186-7</p><p>Vol. 132. Danil Prokhorov (Ed.)Computational Intelligence in Automotive Applications, 2008ISBN 978-3-540-79256-7</p><p>Vol. 133.Manuel Grana and Richard J. Duro (Eds.)Computational Intelligence for Remote Sensing, 2008ISBN 978-3-540-79352-6</p></li><li><p>Manuel GranaRichard J. Duro(Eds.)</p><p>Computational Intelligencefor Remote Sensing</p><p>123</p></li><li><p>Manuel GranaUniversidad Pais Vasco</p><p>Facultad de Informatica</p><p>20018 San SebastianSpain</p><p>e-mail:</p><p>Richard DuroUniversidad de A CorunaGrupo Integrado de Ingeniera</p><p>Escuela Politecnica Superior</p><p>c/ Mendizabal s/n15403 Ferrol (A Coruna)</p><p>Spain</p><p>e-mail:</p><p>ISBN 978-3-540-79352-6 e-ISBN 978-3-540-79353-3</p><p>DOI 10.1007/978-3-540-79353-3</p><p>Studies in Computational Intelligence ISSN 1860-949X</p><p>Library of Congress Control Number: 2008925271</p><p>c 2008 Springer-Verlag Berlin Heidelberg</p><p>This work is subject to copyright. All rights are reserved, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publicationor parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965,in its current version, and permission for use must always be obtained from Springer-Verlag.Violations areliable to prosecution under the German Copyright Law.</p><p>The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply,even in the absence of a specific statement, that such names are exempt from the relevant protective lawsand regulations and therefore free for general use.</p><p>Typeset &amp; Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India.</p><p>Printed on acid-free paper</p><p>9 8 7 6 5 4 3 2 1</p><p></p></li><li><p>Preface</p><p>This book is a composition of diverse points of view regarding the application ofComputational Intelligence techniques and methods into Remote Sensing dataand problems. It is the general consensus that classification, and related dataprocessing, and global optimization methods are the main topics of Computa-tional Intelligence. Global random optimization algorithms appear in this book,such as the Simulated Annealing in chapter 6 and the Genetic Algorithms pro-posed in chapters 3 and 9. Much of the contents of the book are devoted to imagesegmentation and recognition, using diverse tools from regions of ComputationalIntelligence, ranging from Artificial Neural Networks to Markov Random Fieldmodelling. However, there are some fringe topics, such the parallel implemen-tation of some algorithms or the image watermarking that make evident thatthe frontiers between Computational Intelligence and neighboring computationaldisciplines are blurred and the fences run low and full of holes in many places.</p><p>The book starts with a review of the current designs of hyperspectral sensors,more appropriately named Imaging Spectrometers. Knowing the shortcomingsand advantages of the diverse designs may condition the results on some appli-cations of Computational Intelligence algorithms to the processing and under-standing of them Remote Sensing images produced by these sensors. Then thebook contents moves into basic signal processing techniques such as compressionand watermarking applied to remote sensing images. With the huge amount ofremote sensing information and the increasing rate at which it is being produced,it seems only natural that compression techniques will leap into a prominent rolein the near future, overcoming the resistances of the users against uncontrolledmanipulation of their data. Watermarking is the way to address issues of own-ership authentication in digital contents. The enormous volume of informationasks also for advanced information management systems, able to provide intel-ligent query process, as well as to provide for cooperative manipulation of theimages through autonomously provided web services, streamed through specialweb portals, such as the one provided by the European Space Agency (ESA).</p><p>The main contents of the book are devoted to image analysis and efficient (par-allel) implementations of such analysis techniques. The processes include image</p></li><li><p>VI Preface</p><p>segmentation, change detection, endmember extraction for spectral unmixing,and feature extraction. Diverse kinds of Artificial Neural Networks, Mathemati-cal Morphology and Markov Random Fields are applied to these tasks. The kindof images are mostly multispectral-hyperspectral images, with some examples ofprocessing Synthetic Aperture Radar images, whose appeal lies in its insensitiv-ity to atmospheric conditions. Two specific applications stand out. One is forestfire detection and prevention, the other is quality inspection using hyperspectralimages.</p><p>Chapter 1 provides a review of current Imaging Spectrometer designs. Theyfocus on the spectral unit. Three main classes are identified in the literature:filtering, dispersive and interferometric. The ones in the first class only transmita narrow spectral band to each detector pixel. In dispersive imaging spectrome-ters the directions of light propagation change by diffraction, material dispersionor both as a continuous function of wavelength. Interferometric imaging spec-trometers divide a light beam into two, delay them and recombine them in theimage plane. The spectral information is then obtained by performing a Fouriertransform.</p><p>Chapter 2 reviews the state of the art in the application of Data Compressiontechniques to Remote Sensing images, specially in the case of Hyperspectral im-ages. Lossless, Near-Lossless and Lossy compression techniques are reviewed andevaluated on well known benchmark images. The chapter includes summaries ofpertinent materials such as Wavelet Transform, KLT, Coding and Quantizationalgorithms, compression quality measures, etc.</p><p>Chapter 3 formulates the watermarking of digital images as a multi-objectiveoptimization problem and proposes a Genetic Algorithm to solve it. The twoconflicting objectives are the robustness of the watermark against manipulations(attacks) of the watermarked image and the low distortion of the watermarkedimage. Watermarking is proposed as adding the image mark DCT coefficients tosome of the watermarked image DCT coefficients. In the case of hyperspectralimages the DCT is performed independently on each band image. The carefuldefinition of the robustness and distortion fitness functions to avoid flat fitnesslandscapes and to obtain fast fitness evaluations is described.</p><p>Chapter 4 refers the current efforts at the European Space Agency to provideService Support Environments (SSE) that: (1) Simplify the access to multiplesources of Earth Observation (EO) data. (2) Facilitate the extraction of infor-mation from EO data. (3) Reduce the barrier for the definition and prototypingof EO Services. The objective of the chapter is to provide an overview of thesystems which can be put in place to support various kinds of user needs andto show how they relate each other, as well as how they relate with higher leveluser requirements. The chapter reviews several apparently un-related researchtopics: service oriented architecture, service publishing, service orchestration,knowledge based information mining, information and feature extraction, andcontent based information retrieval. The authors stress their relative roles andintegration into a global web-based SSE for EO data.</p></li><li><p>Preface VII</p><p>Chapter 5 reviews some general ideas about Content Based Image Retrieval(CBIR) Systems emphasizing the recent developments regarding Remote Sensingimage databases. The authors introduce an approach for the CBIR in collectionsof hyperspectral images based on the spectral information given by the set ofendmembers induced from each image data. A similarity function is defined andsome experimental results on a collection of synthetic images are given.</p><p>Chapter 6 considers an specific problem, that of sensor deployment when try-ing to build up a wireless sensor network to monitor a patch of land. The Martianexploration is the metaphorical site to illustrate the problem. They propose aformal statement of the problem in the deterministic case (all node positionscan be determined). This leads to the formulation of an objective function thatcan be easily seen to multiple local optima, and to be discontinuous due to theconnectivity constraint. Simulated Annealing is applied to obtain (good approx-imations to) the global optimum.</p><p>Chapters 7 and 8 are devoted to the study of the efficient parallel implemen-tation of segmentation and classification algorithms applied to hyperspectralimages. They include good reviews of the state of the art of the application ofmathematical morphology to spatial-spectral analysis of hyperspectral images.Chapter 7 focuses on the parallel implementation of morphological operators andmorphology derived techniques for spectral unmixing, feature extraction, unsu-pervised and supervised classification, etc. Chapter 8 proposes parallel imple-mentations of Multilayer Perceptron and compares with the morphology basedclassification algorithms. Specific experiments designed to evaluate the influenceof the sample partitioning on the training convergence were carried out by theauthors.</p><p>Chapter 9 deals with the detection and spatial localization (positioning) ofrather elusive but also conspicuous phenomena: the line-shaped weather systemsand spiral tropical cyclones. The works are performed on radar data and satelliteimages and tested on real life conditions. The main search engine are GeneticAlgorithms based on a parametric description of the weather system. Kalmanfilters are used as post-processing techniques to smooth the results of tracking.</p><p>Chaper 10 proposes a Wavelet Transform procedure performed on the HSVcolor space to obtain the primitive features for image mining. A systematicmethod for decomposition level selection based on the frequency content of eachdecomposition level image.</p><p>Chapter 11 reviews the application of Artificial Neural Networks to land coverclassification in remote sensing images and reports results on change detectionusing the Elmann network trained on sequences of images and of SyntheticAperture Radar (SAR) data.</p><p>Chapter 12 is devoted to the problem of Forest Fires management. It describestwo case studies of operational and autonomous processing chains in place forsupporting forest fires management in Europe, focusing on the prevention anddamage assessment phases of the wildfire emergency cycle, showing how com-putational intelligence can be effectively used for: Fire risk estimation and Burnscars mapping. The first fusing risk information and in-situ monitoring. The sec-</p></li><li><p>VIII Preface</p><p>ond based on automatic change detection with medium resolution multispectralsatellite data.</p><p>Chapter 13 focus on the application of image spectrometers to quality controlapplications. Contrary to remote sensing settings, the imaging device is near theimaged object and the illumination can be somehow controlled. The spectralmixing problem takes also another shape, because aggregations of pixels may beneeded to form an appropriate spectrum of a material. The recognition is per-formed applying Gaussian Synapse Neural Networks. 14 extends the applicationof Gaussian Synapse Neural Networks to endmember extraction.</p><p>Chapter 15 is devoted to change detection in Synthetic Aperture Radar(SAR) data. Two automatic unsupervised methods are proposed. One based onthe semi-supervised Expectation Maximization (EM) algorithm and the Fishertransform. The second follows a data-fusion approach based on Markov RandomField (MRF) modeling.</p><p>Spain Manuel GranaRichard Duro</p><p>Acknowledgments</p><p>This book project has been supported partially by the spanish MEC grantsTSI2007-30447-E, DPI2006-15346-C03-03 and VIMS-2003-2...</p></li></ul>