Urban Remote Sensing

This book is a great addition to the very few books on urban remote sensing. (Photogrammetric Engineering and Remote Sensing, 1 March 2013) "This excellent textbook provides a thorough grounding in the uses and types of remote sensing techniques employed for analyzing population, energy use, and other aspects of the urban environment." (Book News, 1 August 2011)

Xiaojun Yang has authored or co-authored more than 70 publications including two edited volumes on urban remote sensing. He was a guest editor for ISPRS Journal of Photogrammetry and Remote Sensing, Photogrametrical Engineering and Remote Sensing, International Journal of Remote Sensing, and Computer, Environment and Urban Systems. Yang has been involved in organizing urban remote sensing sessions at the annual meetings of the Association of American Geographers (AAG) since 2001. This series of events has become a major urban remote sensing forum in USA. Yang currently serves as Chair of Commission on Mapping for Satellite Imagery, International Cartographic Association (ICA).

List of Contributors xiii Author s Biography xvi Preface xix PART 1 INTRODUCTION 1 1 What is urban remote sensing? 3 Xiaojun Yang 1.1 Introduction 4 1.2 Remote sensing and urban studies 5 1.3 Remote sensing systems for urban areas 6 1.4 Algorithms and techniques for urban attribute extraction 7 1.5 Urban socioeconomic analyses 7 1.6 Urban environmental analyses 8 1.7 Urban growth and landscape change modeling 8 Summary and concluding remarks 9 References 10 PART 2 REMOTE SENSING SYSTEMS FOR URBAN AREAS 13 2 Use of archival Landsat imagery to monitor urban spatial growth 15 Xiaojun Yang 2.1 Introduction 16 2.2 Landsat program and imaging sensors 16 2.3 Mapping urban spatial growth in an American metropolis 18 2.4 Discussion 27 3 Limits and challenges of optical very-high-spatial-resolution satellite remote sensing for urban applications 35 Paolo Gamba, Fabio Dell Acqua, Mattia Stasolla, Giovanna Trianni and Gianni Lisini 3.1 Introduction 36 3.2 Geometrical problems 36 3.3 Spectral problems 38 3.4 Mapping limits and challenges 38 3.5 Adding the time factor: VHR and change detection 39 3.6 A possible way forward 39 3.7 Building damage assessment 43 Conclusions 46 References 47 4 Potential of hyperspectral remote sensing for analyzing the urban environment 49 Sigrid Roessner, Karl Segl, Mathias Bochow, Uta Heiden, Wieke Heldens and Hermann Kaufmann 4.1 Introduction 50 4.2 Spectral characteristics of urban surface materials 50 4.3 Automated identification of urban surface materials 54 4.4 Results and discussion of their potential for urban analysis 58 References 60 5 Very-high-resolution spaceborne synthetic aperture radar and urban areas: looking into details of a complex environment 63 Fabio Dell Acqua, Paolo Gamba and Diego Polli 5.1 Introduction 64 5.2 Before spaceborne high-resolution SAR 64 5.3 High-resolution SAR 66 Conclusions 70 Acknowledgments 70 References 70 6 3D building reconstruction from airborne lidar point clouds fused with aerial imagery 75 Jonathan Li and Haiyan Guan 6.1 Lidar-drived building models: related work 76 6.2 Our building reconstruction method 77 6.3 Results and discussion 85 Concluding remarks 89 Acknowledgments 90 References 90 PART 3 ALGORITHMS AND TECHNIQUES FOR URBAN ATTRIBUTE EXTRACTION 93 7 Parameterizing neural network models to improve land classification performance 95 Xiaojun Yang and Libin Zhou 7.1 Introduction 96 7.2 Fundamentals of neural networks 96 7.3 Internal parameters and classification accuracy 100 7.4 Training algorithm performance 105 7.5 Toward a systematic approach to image classification by neural networks 107 8 Characterizing urban subpixel composition using spectral mixture analysis 111 Rebecca Powell 8.1 Introduction 112 8.2 Overview of SMA implementation 112 8.3 Two case studies 118 Conclusions 124 Acknowledgments 126 References 126 9 An object-oriented pattern recognition approach for urban classification 129 Soe W. Myint and Douglas Stow 9.1 Introduction 130 9.2 Object-oriented classification 130 9.3 Data and study area 133 9.4 Methodology 134 9.5 Results and discussion 137 Conclusion 139 References 140 10 Spatial enhancement of multispectral images on urban areas 141 Bruno Aiazzi, Stefano Baronti, Luca Capobianco, Andrea Garzelli and Massimo Selva 10.1 Introduction 142 10.2 Multiresolution fusion scheme 144 10.3 Component substitution fusion scheme 144 10.4 Hybrid MRA component substitution method 146 10.5 Results 147 Conclusions 152 References 152 11 Exploring the temporal lag between the structure and function of urban areas 155 Victor Mesev 11.1 Introduction 156 11.2 Micro and macro urban remote sensing 156 11.3 The temporal lag c