1st Edition

Multispectral Image Analysis Using the Object-Oriented Paradigm

By Kumar Navulur Copyright 2007
    204 Pages 42 Color & 117 B/W Illustrations
    by CRC Press

    204 Pages 42 Color & 117 B/W Illustrations
    by CRC Press

    Bringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery.

    This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying downloadable resources present sample data that enable the use of different approaches to problem solving.

    Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.

    Objects and Human Interpretation Process
    Object-Oriented Paradigm
    Organization of the Book

    Multispectral Remote Sensing
    Spatial Resolution
    Spectral Resolution
    Radiometric Resolution
    Temporal Resolution
    Multispectral Image Analysis

    Why an Object-Oriented Approach?
    Object Properties
    Advantages of Object-Oriented Approach

    Creating Objects
    Image Segmentation Techniques
    Creating and Classifying Objects at Multiple Scales
    Object Classification
    Creating Multiple Levels
    Creating Class Hierarchy and Classifying Objects
    Final Classification Using Object Relationships between Levels

    Object-Based Image Analysis
    Image Analysis Techniques
    Supervised Classification Using Multispectral Information
    Exploring the Spatial Dimension
    Using Contextual Information
    Taking Advantage of Morphology Parameters
    Taking Advantage of Texture
    Adding Temporal Dimension

    Advanced Object Image Analysis
    Techniques to Control Image Segmentation within eCognition
    Techniques to Control Image Segmentation within eCognition
    Multi-Scale Approach for Image Analysis
    Objects vs. Spatial Resolution
    Exploring the Parent-Child Object Relationships
    Using Semantic Relationships
    Taking Advantage of Ancillary Data

    Accuracy Assessment
    Sample Selection
    Sampling Techniques
    Ground Truth Collection
    Accuracy Assessment Measures




    Kumar Navulur

    ". . . Navulur’s book makes a valuable contribution because it offers a well-organized reference to contemporary developments in object-oriented image analysis, along with various creative ideas for implementation of these methods in practical solutions."

    – Matthew Ramspott, Department of Geography, Frostburg State University, in Photogrammetric Engineering & Remote Sensing, September 2007, Vol. 73, No. 9