Multispectral 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 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.
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Chapter 1 Introduction
Chapter 2 Multispectral Remote Sensing
Chapter 3 Why an ObjectOriented Approach?
Chapter 4 Creating Objects
Chapter 5 ObjectBased Image Analysis
Chapter 6 Advanced Object Image Analysis
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airport features algorithm apply areas assigned Built-up change detection child objects Class Hierarchy window Click clusters Color figure follows contextual Create a class create objects data mining data sets display distance Double-click eCognition example explore export feature extraction Feature Space Optimization Feature View window fuzzy classification fuzzy logic high-pass filter icon identify IKONOS image analysis image classification Image Objects image segmentation imagery impervious surfaces Import Landsat Let us create Level LULC membership function membership value merging multispectral Navigate NDVI Nearest Neighbor number of objects object boundaries object-based object-oriented option parent objects pixel-based pixels Principal Component Analysis QuickBird range raster ratio Remote Sensing rule base satellite scale parameter scene Segment image Segment the image semantic grouping sensors shown in Figure soil spatial resolution spectral bands spectral difference spectral signatures supervised classification Table thematic classification thematic layer thematic map variables various features vector water bodies Water class