This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas. Combining a theoretical framework with highly practical applications, the book describes a system for the effective detection of single houses and streets in very high resolution. Topics and features: with a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace Center; provides end-of-chapter summaries and review questions; presents a detailed review on remote sensing satellites; examines the multispectral information that can be obtained from satellite images, with a focus on vegetation and shadow-water indices; investigates methods for land-use classification, introducing precise graph theoretical measures over panchromatic images; addresses the problem of detecting residential regions; describes a house and street network-detection subsystem; concludes with a summary of the key ideas covered in the book.

Provides in-depth coverage of novel computer vision methods for remote sensing applications Includes end-of-chapter summaries and review questions With a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace Center Includes supplementary material: sn.pub/extras

Autorentext

Cem Ünsalan is a full professor at the Department of Electrical and Electronics Engineering at Yeditepe University, Turkey, since 2013. He is the Dean of the Faculty of Engineering at the same university. Dr. Ünsalan also worked as a full professor at the Department of Electrical and Electronics Engineering at Marmara University, Turkey, between 2017 and 2023. He served as the department head for four years there. Dr. Ünsalan received his BSc degree from Hacettepe University, Turkey, his MSc degree from Bogazici University, Turkey, and his Ph.D. from The Ohio State University, USA, in 1995, 1998, and 2003, respectively. His research focuses on embedded systems, computer vision, and remote sensing. He has published extensively on these topics in respected journals and has written several books, including Embedded System Design with ARM Cortex-M Microcontrollers: Applications with C, C++ and MicroPython (Springer, 2022). Berkan Höke is currently working as a senior machine vision engineer at Agsenze Ltd, United Kingdom. He has a diverse professional background including roles as a computer vision engineer at Migros, Turkey (2017-2020), machine learning engineer at Huawei, Turkey (2020-2022), and computer vision engineer at Techsign, Turkey (2022-2023). Mr. Höke received his BSc degree from Bilkent University, Turkey, and his MSc degree from Böaziçi University, Turkey, in 2014 and 2019, respectively. His research focuses on machine learning, computer vision, and embedded systems. Eren Atmaca is currently pursuing his master's degree in communications and electronics engineering at Technical University of Munich, Germany. He received his bachelor's degree from Marmara University, Turkey in 2022. His research focuses on embedded systems, signal processing, and machine learning.

Klappentext
Rapid development of remote sensing technology in recent years has greatly increased availability of high-resolution satellite image data. However, detailed analysis of such large data sets also requires innovative new techniques in image and signal processing.This important text/reference presents a comprehensive review of image processing methods, for the analysis of land use in residential areas. Combining a theoretical framework with highly practical applications, making use of both well-known methods and cutting-edge techniques in computer vision, the book describes a system for the effective detection of single houses and streets in very high resolution. Topics and features:With a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace CenterProvides end-of-chapter summaries and review questionsPresents a detailed review on remote sensing satellitesExamines the multispectral information that can be obtained from satellite images, with a focus on vegetation and shadow-water indicesInvestigates methods for land-use classification, introducing precise graph theoretical measures over panchromatic imagesAddresses the problem of detecting residential regionsDescribes a house and street network-detection subsystemConcludes with a summary of the key ideas covered in the bookThis pioneering work on automated satellite and aerial image-understanding systems will be of great interest to researchers in both remote sensing and computer vision, highlighting the benefit of interdisciplinary collaboration between the two communities. Urban planners and policy makers will also find considerable value in the proposed system.Dr. Cem Ünsalan is an Associate Professor in the Department of Electrical and Electronics Engineering at Yeditepe University, Istanbul, Turkey. Dr. Kim Boyer is Professor and Head of the Department ofElectrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, Troy, NY, USA.

Inhalt

Introduction.- Part I: Sensors.- Remote Sensing Satellites and Airborne Sensors.- Part II: The Multispectral Information.- Linearized Vegetation Indices.- Linearized Shadow and Water Indices.- Part III: Land Use Classification.- Review on Land Use Classification.- Land Use Classification using Structural Features.- Land Use Classification via Multispectral Information.- Graph Theoretical Measures for Land Development.- Part IV: Extracting Residential Regions.- Feature Based Grouping to Detect Suburbia.- Detecting Residential Regions by Graph Theoretical Measures.- Part V: Building and Road Detection.- Review on Building and Road Detection.- House and Street Network Detection in Residential Regions.- Part VI: Summarizing the Overall System.- Final Comments.