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Multispectral Image Analysis Using the Object-Oriented Paradigm [Pehme köide]

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(Mapping Science Institute, Longmont, Colorado, USA)
  • Formaat: Paperback / softback, 204 pages, kõrgus x laius: 234x156 mm, kaal: 371 g, 18 Tables, black and white; 75 Halftones, black and white; 42 Illustrations, color; 117 Illustrations, black and white
  • Sari: Remote Sensing Applications Series
  • Ilmumisaeg: 05-Mar-2020
  • Kirjastus: CRC Press
  • ISBN-10: 0367446243
  • ISBN-13: 9780367446246
Teised raamatud teemal:
Multispectral Image Analysis Using the Object-Oriented ParadigmSuurem pilt
  • Formaat: Paperback / softback, 204 pages, kõrgus x laius: 234x156 mm, kaal: 371 g, 18 Tables, black and white; 75 Halftones, black and white; 42 Illustrations, color; 117 Illustrations, black and white
  • Sari: Remote Sensing Applications Series
  • Ilmumisaeg: 05-Mar-2020
  • Kirjastus: CRC Press
  • ISBN-10: 0367446243
  • ISBN-13: 9780367446246
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367446246.html
Märksõnad:
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.

Arvustused

". . . Navulurs 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

Preface
List of Figures
List of Tables
Chapter 1 Introduction
1(4)
1.1 Background
1(1)
1.2 Objects and Human Interpretation Process
1(2)
1.2.1 Human Interpretation Process versus Computer-Aided Image Analysis
2(1)
1.3 Object-Oriented Paradigm
3(1)
1.4 Organization of the Book
3(2)
Chapter 2 Multispectral Remote Sensing
5(10)
2.1 Spatial Resolution
5(4)
2.2 Spectral Resolution
9(1)
2.3 Radiometric Resolution
10(1)
2.4 Temporal Resolution
11(1)
2.5 Multispectral Image Analysis
12(3)
Chapter 3 Why an Object-Oriented Approach?
15(4)
3.1 Object Properties
16(2)
3.2 Advantages of an Object-Oriented Approach
18(1)
Chapter 4 Creating Objects
19(28)
4.1 Image Segmentation Techniques
19(9)
4.1.1 Public Domain Image Segmentation Software
20(1)
4.1.2 eCognition Segmentation
20(8)
4.2 Creating and Classifying Objects at Multiple Scales
28(2)
4.3 Object Classification
30(6)
4.4 Creating Multiple Levels
36(3)
4.5 Creating Class Hierarchy and Classifying Objects
39(4)
4.6 Final Classification Using Object Relationships between Levels
43(4)
Chapter 5 Object-Based Image Analysis
47(62)
5.1 Image Analysis Techniques
47(22)
5.1.1 Unsupervised Classification
47(1)
5.1.2 Supervised Classification
48(1)
5.1.3 Rule-Based Classification
49(7)
5.1.4 Classification and Regression Trees (CART) and Decision Trees
56(5)
5.1.5 Neural Nets and Fuzzy Logic Classification
61(1)
5.1.5.1 Neural Network Classification
61(2)
5.1.5.2 Fuzzy Classification
63(6)
5.2 Supervised Classification Using Multispectral Information
69(9)
5.3 Exploring the Spatial Dimension
78(6)
5.4 Using Contextual Information
84(14)
5.5 Taking Advantage of Morphology Parameters
98(2)
5.6 Taking Advantage of Texture
100(2)
5.7 Adding Temporal Dimension
102(7)
Chapter 6 Advanced Object Image Analysis
109(32)
6.1 Techniques to Control Image Segmentation within eCognition
109(6)
6.1.1 Using Ancillary GIS Layers to Contain Object Boundaries
109(6)
6.2 Techniques to Control Image Segmentation within eCognition
115(15)
6.2.1 Spatial Filtering
115(1)
6.2.2 Principal Component Analysis (PCA)
116(3)
6.2.3 Ratioing
119(1)
6.2.4 Vegetation Indices
119(1)
6.2.4.1 Ratio Vegetation Index (RVI)
120(1)
6.2.4.2 Normalized Difference Vegetation Index (NDVI)
120(1)
6.2.4.3 Soil-Adjusted Vegetation Index (SAVI)
121(1)
6.2.5 RGB-to-HIS Transformation
122(1)
6.2.6 The Tassel Cap Transformation
122(8)
6.3 Multiscale Approach for Image Analysis
130(2)
6.4 Objects versus Spatial Resolution
132(2)
6.5 Exploring the Parent-Child Object Relationships
134(3)
6.6 Using Semantic Relationships
137(2)
6.7 Taking Advantage of Ancillary Data
139(2)
Chapter 7 Accuracy Assessment
141(10)
7.1 Sample Selection
141(1)
7.2 Sampling Techniques
142(1)
7.3 Ground Truth Collection
142(1)
7.4 Accuracy Assessment Measures
143(8)
References 151(4)
Index 155
Navulur\, Kumar