A new method for the quantitative imageanalysis of complex microstructures in steel is presented. Microstructures in modern steel grades on the one hand become more and more complex while on the other hand the size o...
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(纸本)9780873397322
A new method for the quantitative imageanalysis of complex microstructures in steel is presented. Microstructures in modern steel grades on the one hand become more and more complex while on the other hand the size of the constituents is scaled down ever further. For these reasons it is increasingly difficult or even impossible to access reliable quantitative information on ratios, shapes and spatial distributions of individual phases and constituents by utilising traditional metallographic analysis methods. The presented new analysis approach is object-based. Its basic processing units are so-called "imageobjects" which reflect the pictured structures by merged groups of likely related pixels. By the use of imageobjects, information on shape graphical texture and spatial distribution of the microstructural constituents can be processed. With these additional data available, it is possible to quantitatively analyse complex microstructures. This is demonstrated by the application of the method to SEM images of different multiphase steel grades. Because the analysis routine is fully automated, the analysis of large amounts of image data is possible and thereby the investigation of statistically relevant areas of microstructures.
Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and res...
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Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the Gwynns Falls watershed from 1999 to 2004. The Gwynns Falls watershed includes portions of Baltimore City and Baltimore County, Maryland, USA. An object-based approach was first applied to implement the land cover classification separately for each of the two years. The overall accuracies of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. Following the classification, we conducted a comparison of two different land cover change detection methods: traditional (i.e., pixel-based) post-classification comparison and object-based post-classification comparison. The results from our analyses indicated that an object-based approach provides a better means for change detection than a pixel based method because it provides an effective way to incorporate spatial information and expert knowledge into the change detection process. The overall accuracy of the change map produced by the object-based method was 90.0%, with Kappa statistic of 0.854, whereas the overall accuracy and Kappa statistic of that by the pixel-based method were 81.3% and 0.712, respectively.
Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing p...
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Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing phenomenon. This paper proposes precise monitoring of building construction using an object-based classification methodology applied to Spot 5 images with a 2.5 m resolution. An application at a regional scale on Reunion Island in the Indian Ocean shows that this building extraction methodology has limitations in the production of reference urban maps because of difficulties in defining the shape and the number of buildings compared to classical photo-interpretation of aerial photography. However, these results are of great value for planning in urban sprawl areas where up-to-date information is lacking because of the rapid pace of house construction and residential development. (C) 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
The methods of forest inventory data acquisition, based on the analysis of remotely sensed images have been well tested and implemented during the last decade. The predominant visual interpretation and pixel-based aut...
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The methods of forest inventory data acquisition, based on the analysis of remotely sensed images have been well tested and implemented during the last decade. The predominant visual interpretation and pixel-based automated techniques are now being gradually replaced by the object-basedimage classification at multiple levels. This paper describes an experiment using medium-format digital aerial imagery for the purpose of automated updating of the existing GIS forest management database (LHPO). The method emphasises the pre-processing phase, where various image transforms and additional channels i.e. spectral ratios and vegetation indices (NDVI), low-pass filters, Sobel edge and GLCM (grey level co-occurrence matrix) texture measures are derived from the original dataset. The layer stack is then transferred into the object-oriented classification environment together with the existing thematic vector layer, and analysed on three hierarchical object levels. The classification involves the recognition of the successional stage of forest compartments and the estimation of tree species composition in terms of area coverage. In addition, age information on the GIS forestry management map can be updated and the spatial distribution of classes corrected using the multi-scale object relations of the former analysis. The advances of the automated procedure based on sequential processing of imageobjects are partially covered. Moreover, aspects of utilisation of the medium-format colour infra-red images (CIR) as an alternative to traditional aerial photos and very high resolution (VHR) satellite data, were considered.
We introduce a novel approach to nonlinear signal analysis, which is referred to as supremal multiscale analysis. The proposed approach provides a rigorous mathematical foundation for a class of nonlinear multiscale s...
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We introduce a novel approach to nonlinear signal analysis, which is referred to as supremal multiscale analysis. The proposed approach provides a rigorous mathematical foundation for a class of nonlinear multiscale signal analysis schemes and leads to a decomposition that can effectively be used in signal processing and analysis. Moreover, it is related to the supremal scale-spaces proposed by Heijmans and van den Boomgaard and is similar in flavor to the well-known linear multiresolution theory of Mallat and Meyer. In this framework, linear concepts such as vector spaces, projections, and linear operators are replaced by conceptually analogous nonlinear notions. We use supremal multiscale analysis to construct a multiscale image decomposition scheme based on two mathematical concepts that play a key role in the analysis and interpretation of images by vision systems, namely, regional maxima and connectivity. The resulting scheme is referred to as skyline supremal multiscale analysis and satisfies several useful properties desired by any multiscale imageanalysis tool. It is grayscale invariant, as well as translation and scale invariant. Moreover, it progressively removes connected components from the level sets of an image without introducing new ones. But, most importantly, it decomposes the regional maxima of an image in a natural causal hierarchy by gradually removing these maxima without introducing new ones. image decomposition by skyline supremal multiscale analysis can be used to construct nonlinear tools for image processing and analysis that provide solutions to problems where traditional linear techniques are ineffective. We discuss one such tool and illustrate its use in object-based extraction and denoising.
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