Multispectral Image Analysis Using the Object-Oriented Paradigm

by Kumar Navulur

• ISBN13: 9781420043075
• ISBN10: 1420043072
• eBook ISBN(s): 9781040207994
• Format: Nonspecific Binding
• Copyright: 2006-12-05
• Publisher: Taylor & Francis

Summary

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.

Table of Contents

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

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