Multi-Spectral Image Analysis Using the Object Oriented Paradigm
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...
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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.
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 ApproachCreating 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 LevelsObject-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 DimensionAdvanced 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 ReferencesIndex
