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Multispectral image analysis using the object-oriented paradigm

Author: Kumar Navulur
Publisher: Boca Raton : CRC Press/Taylor & Francis, ©2007.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"Bring 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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Navulur, Kumar.
Multispectral image analysis using the object-oriented paradigm.
Boca Raton : CRC Press/Taylor & Francis, ©2007
(DLC) 2006021814
(OCoLC)70232354
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kumar Navulur
ISBN: 9781420043075 1420043072 9780429146305 0429146302
OCLC Number: 137259962
Description: 1 online resource (165 pages, 24 unnumbered pages of plates) : illustrations (some color)
Contents: Preface --
List of Figures --
List of Tables --
Chapter 1 Introduction --
1.1 Background --
1.2 Objects and Human Interpretation Process --
1.2.1 Human Interpretation Process versus Computer-Aided Image Analysis --
1.3 Object-Oriented Paradigm --
1.4 Organization of the Book --
Chapter 2 Multispectral Remote Sensing --
2.1 Spatial Resolution --
2.2 Spectral Resolution --
2.3 Radiometric Resolution --
2.4 Temporal Resolution --
2.5 Multispectral Image Analysis --
Chapter 3 Why an Object-Oriented Approach? --
3.1 Object Properties --
3.2 Advantages of an Object-Oriented Approach --
Chapter 4 Creating Objects --
4.1 Image Segmentation Techniques --
4.1.1 Public Domain Image Segmentation Software --
4.1.2 eCognition Segmentation --
4.2 Creating and Classifying Objects at Multiple Scales --
4.3 Object Classification --
4.4 Creating Multiple Levels --
4.5 Creating Class Hierarchy and Classifying Objects --
4.6 Final Classification Using Object Relationships between Levels --
Chapter 5 Object-Based Image Analysis --
5.1 Image Analysis Techniques --
5.1.1 Unsupervised Classification --
5.1.2 Supervised Classification --
5.1.3 Rule-Based Classification --
5.1.4 Classification and Regression Trees (CART) and Decision Trees --
5.1.5 Neural Nets and Fuzzy Logic Classification --
5.2 Supervised Classification Using Multispectral Information --
5.3 Exploring the Spatial Dimension --
5.4 Using Contextual Information --
5.5 Taking Advantage of Morphology Parameters --
5.6 Taking Advantage of Texture --
5.7 Adding Temporal Dimension --
Chapter 6 Advanced Object Image Analysis --
6.1 Techniques to Control Image Segmentation within eCognition --
6.1.1 Using Ancillary GIS Layers to Contain Object Boundaries --
6.2 Techniques to Control Image Segmentation within eCognition --
6.2.1 Spatial Filtering --
6.2.2 Principal Component Analysis (PCA) --
6.2.3 Ratioing --
6.2.4 Vegetation Indices --
6.2.5 RGB-to-HIS Transformation --
6.2.6 The Tassel Cap Transformation --
6.3 Multiscale Approach for Image Analysis --
6.4 Objects versus Spatial Resolution --
6.5 Exploring the Parent-Child Object Relationships --
6.6 Using Semantic Relationships --
6.7 Taking Advantage of Ancillary Data --
Chapter 7 Accuracy Assessment --
7.1 Sample Selection --
7.2 Sampling Techniques --
7.3 Ground Truth Collection --
7.4 Accuracy Assessment Measures --
References --
Index.
Responsibility: Kumar Navulur.

Abstract:

Bringing a fresh perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. This work reviews the image analysis methods and  Read more...

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