rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multispectral Satellite Image Understanding
    sub title: From Land Classification to Building and Road Detection
  name:
    name part: Ünsalan, Cem.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Boyer, Kim L.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 186 p. online resource.
  abstract: 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 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 This 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 of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, Troy, NY, USA.
  table of contents: 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.
  note: by Cem Ünsalan, Kim L. Boyer.
  subject:
    topic: Computer science
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Pattern Recognition
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Computer Vision and Pattern Recognition
  identifier: 9780857296672
  identifier: http://dx.doi.org/10.1007/978-0-85729-667-2
  record info:
    record creation date: 130531
    record change date: 20151125222217.0
    record identifier: 978-0-85729-667-2