DOCSLIB.ORG

Interactive Models for Latent Information Discovery in Satellite Images

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Interactive Models for Latent Information Discovery in Satellite Images Interactive Models for Latent Information Discovery in Satellite Images DISSERTATION zur Erlangung des Grades eines Doktors der Ingenieurwissenschaften vorgelegt von Dipl.-Eng. Dragos Bratasanu eingereicht bei der Naturwissenschaftlich-Technischen Fakultät der Universität Siegen Siegen 2014 Koordinator: Prof. Dr. Otmar Loffeld Koordinator: Prof. Dr. Mihai Datcu Datum der Mündlichen Prüfung: 30 Mai 2014 To my parents 1 Abstract The recent increase in Earth Observation (EO) missions has resulted in unprecedented volumes of multi-modal data to be processed, understood, used and stored in archives. The advanced capabilities of satellite sensors become useful only when translated into accurate, focused information, ready to be used by decision makers from various fields. Two key problems emerge when trying to bridge the gap between research, science and multi-user platforms: (1) The current systems for data access permit only queries by geographic location, time of acquisition, type of sensor, but this information is often less important than the latent, conceptual content of the scenes; (2) simultaneously, many new applications relying on EO data require the knowledge of complex image processing and computer vision methods for understanding and extracting information from the data. This dissertation designs two important concept modules of a theoretical image information mining (IIM) system for EO: semantic knowledge discovery in large databases and data visualization techniques. These modules allow users to discover and extract relevant conceptual information directly from satellite images and generate an optimum visualization for this information. The first contribution of this dissertation brings a theoretical solution that bridges the gap and discovers the semantic rules between the output of state-of-the-art classification algorithms and the semantic, human-defined, manually-applied terminology of cartographic data. The set of rules explain in latent, linguistic concepts the contents of satellite images and link the low-level machine language to the high-level human understanding. The second contribution of this dissertation is an adaptive visualization methodology used to assist the image analyst in understanding the satellite image through optimum representations and to offer cognitive support in discovering relevant information in the scenes. It is an interactive technique applied to discover the optimum combination of three spectral features of a multi-band satellite image that enhance visualization of learned targets and phenomena of interest. The visual mining module is essential for an IIM system because all EO-based applications involve several steps of visual inspection and the final decision about the information derived from satellite data is always made by a human operator. To ensure maximum correlation between the requirements of the analyst and the possibilities of the computer, the visualization tool models the human visual system and secures that a change in the image space is equivalent to a change in the perception space of the operator. This thesis presents novel concepts and methods that help users access and discover latent information in archives and visualize satellite scenes in an interactive, human-centered and information-driven workflow. 2 Table of Contents 1. Introduction…………………………………………………………………………..... 5 1.1 Motivation………………………………………………………………………….. 6 1.2 Positioning this dissertation……………………………………………………….. 7 1.3 Contributions……………………………………………………………………….. 7 1.3.1 Latent information discovery in satellite images………………………... 8 1.3.2 Visual data mining……………………………………………………….. 9 1.4 Outline of this dissertation………………………………………………………… 10 2. Information Spaces in Earth Observation Data….….….….….….….….….….……. 11 2.1 Introduction….….….….….….….….….….….….….….….….….….….….….…... 11 2.1.1 Data collection & analysis procedures….….….….….….….….….….….. 13 2.1.2 Data collection….….….….….….….….….….….….….….….….….…… 13 2.2 Resolution….….….….….….….….….….….….….….….….….….….….….……. 14 2.2.1 Spectral resolution….….….….….….….….….….….….….….….….….. 14 2.2.2 Spatial resolution….….….….….….….….….….….….….….….….…… 15 2.2.3 Temporal resolution….….….….….….….….….….….….….….….…… 15 2.2.4 Radiometric resolution….….….….….….….….….….….….….….……. 16 2.2.5 Angular information….….….….….….….….….….….….….….….……. 16 2.3 Data analysis methods….….….….….….….….….….….….….….….….….……. 16 2.4 Remote sensing satellite systems….….….….….….….….….….….….….….……. 17 2.5 Spectral dimensions in satellite images….….….….….….….….….….….….……. 18 2.6 Semantic dimensions in satellite image-based cartography….….….….….….……. 22 2.6.1 Corine Land Cover….….….….….….….….….….….….….….….….….. 22 2.6.2 Urban Atlas….….….….….….….….….….….….….….….….….….…… 25 2.6.3 Rapid mapping applications ….….….….….….….….….….….….….…. 27 2.6.4 EO-based applications….….….….….….….….….….….….….….…….. 27 3. Image Information Mining: State-of-the-Art………………………………………... 30 3.1 Introduction………………………………………………………………...………. 30 3.2 Image Information Mining System Design………………………………………… 33 3.2.1 Feature extraction………………………………………............................ 33 3.2.2 Feature selection…………………………………………………………. 33 3.2.3 Feature selection review………………………………………………… 37 3.2.4 Multidimensional index…………………………………………………... 39 3.3 Content-based Information Retrieval: State-of-the-art in multimedia…………….. 41 3.3.1 Human-centered systems for multimedia information retrieval………... 43 3.3.2 Semantic learning for CBIR……………………………………………... 44 3.3.3 Relevance feedback……………………………………………………… 45 3.3.4 Content-based image retrieval – forensics……………………………… 47 3.3.5 Content-based image retrieval – medical imaging……………………… 48 3.3.6 Concluding remarks………………………………………………………. 48 3.4 Information mining systems for Earth Observation - A brief review……………… 48 3 4. Basics of Inference and Stochastic Image Analysis………………………………….. 52 4.1 Stochastic image analysis…………………………………………………………. 53 4.1.1 Probability………………………………………………………………… 53 4.1.2 Random variables………………………………………………………… 54 4.1.2.1 Distribution function and probability density function.……………... 55 4.1.2.2 Statistical moments…………………………………………………... 58 4.2 Transformation-based analysis……………………………………………………. 61 4.2.1 Principal component analysis………………………………………….…. 61 4.2.2 Independent component analysis…………………………………………. 62 4.3 Bayesian Inference…………………………………………………………………. 62 4.3.1 Parameter estimation……………………………………………………... 64 4.3.2 Case studies………………………………………………………………. 65 4.3.3 Generative probabilistic models………………………………………….. 67 4.3.3.1 Gaussian mixture models……………………………………………. 68 4.3.3.2 Latent semantic analysis …………………………………………… 70 4.3.3.3 Probabilistic latent semantic indexing……………………………… 70 4.3.3.4 Latent Dirichlet Allocation…………………………………………... 72 4.4 Information theory…………………………………………….…………………… 77 4.4.1 Shannon’s measure of information……………………………………….. 77 4.4.2 Mutual information……………………………………………………….. 78 4.4.3 Kullback-Leibler divergence……………………………………………... 79 5. Bridging the Gap for Semantic Annotation of Satellite Images…………………….. 80 5.1 Introduction………………………………………………………………………… 81 5.2 Spectral signatures and semantic content extraction……………………………... 83 5.3 Map label learning using Latent Dirichlet Allocation……………………………... 86 5.3.1 Latent Dirichlet Allocation……………………………………………….. 86 5.3.2 Document definition – matching images & words……………………… 86 5.3.3 LDA generative process for image annotation………………………….. 88 5.3.4 Semantic learning………………………………………………………... 89 5.4 Composition rules for bridging the semantic gap………………………………... 92 5.5 Case studies: rules for bridging machine and human languages………………... 93 6. Spectral Band Discovery for Advancing Multispectral Satellite Image Analysis and Photo-Interpretation……………………………………………………………… 101 6.1 Exploratory visual analysis of satellite images…………………………………... 102 6.2 Contextual information integration for spectral feature selection………………... 103 6.3 Minimum-Redundancy-Maximum-Relevance criterion for feature selection…… 105 6.4 Objective evaluation of subjective visual information…………………………… 107 6.4.1 Visual Image Analysis – elements of processing………………………... 108 6.4.2 Color metrics for satellite image analysis………………………………… 109 6.4.3 Color models for satellite image analysis………………………………… 112 6.4.4 Quantitative evaluation using color distances………………………….. 112 6.5 Experiments and results……………………………………………………………. 114 6.6 Discussion………………………………………………………………………….. 129 6.6.1 Comparison and evaluation: mRMR, PCA, ICA…………………………. 129 6.6.2 mRMR score similarity statistics and physical modeling………………… 136 7. Conclusions……………………………………………………………………………... 145 8. Appendix………………………………………………………………………………... 147 9. Acronym list…………………………………………………………………………….. 187 10. Acknowledgements……………………………………………………………………... 188 11. Bibliography…………………………………………………………………………….. 189 4 1 Introduction Our planet is going through unprecedented climate and environmental changes, affecting our society in previously unknown ways. To monitor and predict the effects of these diverse environmental challenges, new generations of space borne imaging sensors with advanced recording capabilities have been recently launched or are scheduled for the next few years (e.g. ESA Sentinels, SPOT 6, 7, Pleiades). This increase in Earth Observation (EO) missions will result in large volumes of data requiring to be processed, understood, used and stored in archives. The need for timely delivery of accurate, focused information for decision making and intervention is constantly growing and the continuous increase
Load more

Recommended publications
  • Modelling the Colour Difference by Artificial Neural Network
    Lappeenranta University of Technology Faculty of Technology Management Degree Program in Intelligent Computing Master’s Thesis Alireza Adli MODELLING THE COLOUR DIFFERENCE BY ARTIFICIAL NEURAL NETWORK Examiners: Assoc. Prof. Arto Kaarna MSc. Aidin Hassanzadeh Supervisors: Assoc. Prof. Arto Kaarna MSc. Aidin Hassanzadeh ABSTRACT Lappeenranta University of Technology Faculty of Technology Management Degree Program in Intelligent Computing Alireza Adli MODELLING THE COLOUR DIFFERENCE BY ARTIFICIAL NEURAL NET- WORK Master’s Thesis 2016 68 pages,23 figures,11 tables. Examiners: Assoc. Prof. Arto Kaarna MSc. Aidin Hassanzadeh Keywords: color difference, artificial neural network, chromaticity discrimination, HVS This thesis work studies the modelling of the colour difference using artificial neural net- work. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the ori- entation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results. PREFACE I would like to thank my supervisor Professor Arto Kaarna for guiding me from the very beginning of this master thesis to the end of it and for answering my questions patiently. I would also like to thank my supervisor and dear friend MSc.
    [Show full text]
  • 5 Colour Constancy
    Human colour perception. A psychophysical study of human colour perception for real and computer- simulated two-dimensional and three-dimensional objects. Item Type Thesis Authors Hedrich, Monika Rights <a rel="license" href="http://creativecommons.org/licenses/ by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by- nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http:// creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. Download date 04/10/2021 11:53:02 Link to Item http://hdl.handle.net/10454/4304 1 Table of contents 1 INTRODUCTION .................................................................................................... 6 1.1 OVERVIEW OF THE FOLLOWING CHAPTERS......................................................... 8 2 PHYSIOLOGY OF COLOUR VISION .................................................................. 10 2.1 THEORIES OF COLOUR VISION ......................................................................... 15 3 COLOUR SPACES .............................................................................................. 17 3.1 INTRODUCTION ............................................................................................... 17 3.1.1 Arranging colour in a three-dimensional space......................................... 18 3.2 DEVELOPMENT OF THE CIE 1931 XYZ COLOUR SPACE.................................... 19 3.2.1 Metamers.................................................................................................
    [Show full text]
  • Optibinwhere Consistency Begins
    OptibinWhere consistency begins Technical Overview Photography: Nagieb Azhar and WCT Construction 2 Color Kinetics Optibin Overview Optibin Achieves color consistency with industry-leading LED optimization Color consistency is an indicator of light quality for both color and white-light LEDs. Where white light is concerned, correlated color temperature, or CCT, describes whether white light appears warm (reddish), neutral, or cool (bluish). The standard definitions of CCT allow a range of variation in chromaticity that can be readily discerned by viewers even when the CCT value is the same. Ensuring color consistency is a major concern of LED manufacturers, who must create methods to keep color variations under tight control. Optibin® is a proprietary binning optimization process developed by Color Kinetics to achieve exceptional color consistency with advanced LED optimization. Optibin uses an advanced bin selection formula that exceeds industry standards for chromaticity to guarantee uniformity and consistency of hue and color temperature. Color Kinetics Optibin 3 Technically speaking, the “temperature” in Exploring correlated color temperature (CCT) refers to black-body* radiation—the light emitted by a solid object with certain properties, Correlated heated to the point of incandescence— and is expressed in degrees K (Kelvin), Color a standard measurement of absolute temperature. As a black body gets hotter, Temperature the light it emits progresses through a sequence of colors, from red to orange to yellow to white to blue. This is very similar to what happens to a piece of iron heated in a blacksmith’s forge. Photography: Courtesy of Nagieb Azhar and WCT Construction 4 Color Kinetics Optibin CCT explained The sequence of colors describes a curve within a color Spectral analysis of visible light makes it possible to space.
    [Show full text]
  • Color Maintenance of Leds in Laboratory and Field Applications
    Color Maintenance of LEDs in Laboratory and Field Applications September 2013 Prepared for: Solid-State Lighting Program Building Technologies Office Office of Energy Efficiency and Renewable Energy U.S. Department of Energy Prepared by: Pacific Northwest National Laboratory Color Maintenance of LEDs in Laboratory and Field Applications 1 2 3 1 Michael Royer, Ralph Tuttle, Scott Rosenfeld, Naomi Miller 1 Pacific Northwest National Laboratory, Portland, OR 2 Cree, Inc., Durham, NC 3 Smithsonian Institution, Washington, DC September 2013 PNNL-22759 Abstract To date, consideration for parametric failure of LED products has largely been focused on lumen maintenance. However, color shift is a cause of early failure for some products, and is especially important to consider in certain applications, such as museum lighting, where visual appearance is critical. Field data collected by the U.S. Department of Energy’s (DOE) GATEWAY program for LED lamps installed in museums shows that many have changed color beyond a reasonable tolerance well before their rated lifetimes have been reached. Laboratory data collected by DOE’s CALiPER program between 2008 and 2010 reveals that many early LED products shifted beyond acceptable tolerances in as little as a few thousand hours. In contrast, data from the L Prize® program illustrates that commercially available LED products can have exemplary color stability that is unmatched by traditional light sources. In addition to presenting data from the aforementioned DOE programs, this report discusses the metrics used for communicating color shift, and provides guidance for end users on how to monitor chromaticity and what to look for in manufacturer warranties.
    [Show full text]
  • White Light Parameters.Fm
    www.osram-os.com Application Note No. AN140 Light quality — White light parameters Application Note Valid for: DURIS® S / DURIS® E / OSCONIQ® S / OSCONIQ® P / OSLON® SSL / OSLON® Square / SOLERIQ® S Abstract White light is not the same as white light. When different light sources are used, color differences may become visible. To understand why this can happen, it is necessary to understand how people perceive color and light. Nevertheless, it is possible to reduce the color shifts by choosing suitable white LEDs combined with an appropriate system setup. This application note provides basic information on optical quantities, color spaces and CIE chromaticity diagrams. Furthermore, it describes how color consistency for white light applications can be achieved. Further information: Please also refer to the application note Light quality Part II: “Light quality — Color metrics.” Authors: Wilm Alexander / Chew Ivan 2019-04-05 | Document No.: AN140 1 / 15 www.osram-os.com Table of contents A. Optical quantities ....................................................................................................2 Radiometric quantities ........................................................................................4 Photometric quantities ........................................................................................5 B. Color spaces ...........................................................................................................7 CIE 1931 color space — xy color space ............................................................7
    [Show full text]
  • E. Gorbunova, A. Chertov Colorimetry of Radiation Sources
    E. Gorbunova, A. Chertov COLORIMETRY OF RADIATION SOURCES St. Petersburg 2016 THE MINISTRY OF EDUCATION AND SCIENCE OF THE RUSSIAN FEDERATION ITMO UNIVERSITY E. Gorbunova, A. Chertov COLORIMETRY OF RADIATION SOURCES TEXTBOOK St. Petersburg 2016 2 E. Gorbunova, A. Chertov Colorimetry of radiation sources. Textbook. – SPb: ITMO University, 2016. – 124 pages Theoretical framework and calculation methodology for tristimulus values and source radiation chromaticity coordinates are given. General outline of vision physiological basis is also presented. In addition, colorimetry basic terms and definitions as well as principles of color space construction are given. Besides, rules for color temperature (and correlated color temperature) calculation and calculation of color rendering index for the radiation source are given. The textbook is intended for students majoring for a Master's degree 12.04.02 - "Optical Engineering". Recommended for publication by the Academic Board of the Laser and Light Engineering Department, Minutes No. 13 of December 13, 2016. ITMO University is the leading Russian university in the field of information and photonic technologies, one of the few Russian universities with the status of the national research university granted in 2009. Since 2013 ITMO University has been a participant of the Russian universities' competitiveness raising program among the world's leading academic centers known as "5-100". Objective of ITMO University is the establishment of a world-class research university being entrepreneurial in nature,
    [Show full text]
  • Assessing Color Discrimination
    Dissertations and Theses Spring 2012 Assessing Color Discrimination Joshua R. Maxwell Embry-Riddle Aeronautical University - Daytona Beach Follow this and additional works at: https://commons.erau.edu/edt Part of the Aviation Commons, and the Cognitive Psychology Commons Scholarly Commons Citation Maxwell, Joshua R., "Assessing Color Discrimination" (2012). Dissertations and Theses. 103. https://commons.erau.edu/edt/103 This Thesis - Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. i ASSESSING COLOR DISCRIMINATION By: Joshua R. Maxwell A Thesis Submitted to the Department of Human Factors & Systems in Partial Fulfillment of the Requirements for the Degree of Master of Science in Human Factors & Systems Embry-Riddle Aeronautical University Daytona Beach, Florida Spring 2012 i ii Abstract The purpose of this study was to evaluate human color vision discriminability within individuals that have color normal vision and those that have color deficient vision. Combinations of 15 colors were used from a list of colors recommended for computer displays in Air Traffic Control settings, a population with some mildly color vision deficient individuals. After a match to sample test was designed to assess the limits of human color vision discrimination based on color saturation and hue, standard color diagnostic tests were used to categorize college students as having normal or deficient color vision. The results argue that color saturation and hue impact human ability to discriminate colors, particularly as the delta E is small.
    [Show full text]
  • Optibin Optibin Achieves Color Consistency with Industry-Leading LED Optimization
    Optibin Optibin Achieves color consistency with industry-leading LED optimization Color consistency is an index of light quality for both color and white-light LEDs. Where white light is concerned, correlated color temperature, or CCT, describes whether white light appears warm (reddish), neutral, or cool (bluish). The standard definitions of CCT allow a range of variation in chromaticity that can be readily discerned by viewers even when the CCT value is the same. Ensuring color consistency is a major concern of LED manufacturers, which must create methods to keep color variations under tight control. Optibin® is a proprietary binning optimization process developed by Color Kinetics to achieve exceptional color consistency with advanced LED optimization. Optibin uses an advanced bin selection formula that exceeds industry standards for chromaticity to guarantee uniformity and consistency of hue and color temperature for Signify lighting products. 4G9 Office Tower Precinct 4, Putrajaya, Malaysia 2 Exploring Correlated Color Temperature Technically speaking, the “temperature” in correlated color temperature (CCT) refers to black-body* radiation—the light emitted by a solid object with certain properties, heated to the point of incandescence—and is expressed in degrees K (Kelvin), a standard measurement of absolute temperature. As a black body gets hotter, the light it emits progresses through a sequence of colors, from red to orange to yellow to white to blue. This is very similar to what happens to a piece of iron heated in a blacksmith’s forge. The sequence of colors describes a curve within a color space. The diagram below shows the CIE 1931 color space, created by the International Commission on Illumination (CIE) to define the entire range of colors visible to the average viewer, with the black-body curve superimposed on it.
    [Show full text]
  • 6/8/2007 Page 1 of 38
    Volume 8 Issue 1 October 2004 Table of Contents Introduction Page 02 Light Sources and Color Q and A PART I When is color important to lighting applications? Page 02 What lamp color characteristics do lighting specifiers use to select light sources? Page 03 How do we see color? Page 05 How does the lighting industry measure color appearance? Page 07 What is correlated color temperature? Page 10 What is color consistency? Page 11 What is color stability? Page 13 PART II How are the color rendering properties of light sources defined? Page 15 What is color rendering index? Page 16 What is full-spectrum index? Page 16 What is gamut area? Page 16 What is the best way to communicate the color rendering properties of a light source? Page 17 What is the relationship between lamp efficacy and color rendering? Page 18 What is the relationship between color rendering and light levels? Page 25 Conclusion Page 26 Appendix A Page 27 Appendix B Page 28 Resources Page 34 Sponsors and Credits Page 36 Glossary Page 37 Legal Notices Page 38 Page 1 of 38 6/8/2007 Introduction The phrase, "Color is only a pigment of your imagination" (Ingling, circa 1977), is a humorous and convenient way to remember that color is not a physical property of objects, but rather a human physiological and psychological response to light. Out of the lighting industry's need to quantify color properties, lighting scientists have developed methods that allow us to approximate color perceptions. A host of measurements are now available to describe such factors as the color appearance of light sources and objects, the ability of a light source to render colors accurately, and the stability of color properties over a lamp's lifetime.
    [Show full text]
  • Use Style: Paper Title
    International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-5, Issue-5, May-2019] https://dx.doi.org/10.22161/ijaems.5.5.8 ISSN: 2454-1311 Multi Color Image Segmentation using L*A*B* Color Space Aden Darge1, Dr Rajesh Sharma R2, Desta Zerihum3, Prof Y K Chung4 1PG Student Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia. 2Assistant Professor, Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia. 3Program Chair, Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia. 4Professor, Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia. Abstract— Image segmentation is always a fundamental color spaces are device independent and should produce but challenging problem in computer vision. The simplest color constancy, at least in principle. Among device approach to image segmentation may be clustering of dependent color spaces are HSI, NCC rgbI and YIQ (see pixels. my works in this paper address the problem of image Appendix 1 for formulae). The different versions of HS- segmentation under the paradigm of clustering. A robust spaces (HSI, HSV, Fleck HS and HSB) are related to the clustering algorithm is proposed and utilized to do human vision system; they describe the color’s in a way that clustering on the L*a*b* color feature space of pixels. is intuitive to humans. Image segmentation is straight forwardly obtained by The three coordinates of CIELAB represent the setting each pixel with its corresponding cluster.
    [Show full text]