Multispectral Image Analysis Using the Object-Oriented Paradigm

ISBN-13: 9781420043068 / Angielski / Twarda / 2006 / 184 str.

Kumar Navulur

Opis

< P> 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 two CD-ROMs 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.< /P>

Spis treści

Introduction
Background
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

References

Index

O autorze

Navulur\, Kumar