Эрдэм шинжилгээний өгүүлэл

The aim of this study is to explore the performances of different data fusion techniques for the enhancement of urban features and evaluate the features obtained by the fusion techniques in terms of separation of different land cover classes. For the data fusion, multiplicative method, Brovey transform, principal component analysis (PCA), Gram-Schmidt fusion, wavelet-based fusion and Elhers fusion are used and the results are compared. Of these methods, the best result is obtained by the use of the wavelet-based fusion. Overall, the research indicates that multisource data sets can significantly improve the interpretation and analysis of land cover types.

The aim of this study is to compare the information extracted from high and very high resolution satellite images in terms of information content and reliability to update thematic layers in a geographical information system (GIS). For the extraction of information from the selected remote sensing (RS) data sets, a knowledge-based classification technique based on a rule-based approach has been constructed. The method uses an initial image segmentation procedure based on a Mahalanobis distance classifier (or spectral parameters) as well as the constraints on spectral and spatial thresholds. Overall, the study indicated that unlike the information extracted from high resolution RS images which contains large uncertainty in terms of delineation of individual objects, the information extracted from very high resolution satellite images are very reliable and can be successfully used for update of a GIS layer specifically in urban context.

The aim of this study is to analyze urban land use changes occurred in central part of Ulaanbaatar, the capital city of Mongolia from 1930 to 2008 with a 10 year interval using GIS and RS data sets. As data sources, large scale topographic maps of 2000 as well as panchromatic and multispectral Quickbird images of 2006 are used. To extract land use information from the Quickbird images, a visual interpretation is applied on the enhanced images. For the database development, spatial analysis and image processing ArcGIS and Erdas Imagine systems installed in a PC environment are used.

Эрдэм шинжилгээний өгүүлэл

Эрдэм шинжилгээний өгүүлэл

Эрдэм шинжилгээний өгүүлэл

The aim of this study is to explore the performances of different data fusion techniques for the enhancement of urban features and evaluate the features obtained by the fusion techniques in terms of separation of different land cover classes. For the data fusion, multiplicative method, Brovey transform, principal component analysis (PCA), Gram-Schmidt fusion, wavelet-based fusion and Elhers fusion are used and the results are compared. Of these methods, the best result is obtained by the use of the wavelet-based fusion. Overall, the research indicates that multisource data sets can significantly improve the interpretation and analysis of land cover types.

The aim of this study is to explore the performances of different data fusion techniques for the enhancement of urban features and evaluate the features obtained by the fusion techniques in terms of separation of different land cover classes. For the data fusion, multiplicative method, Brovey transform, principal component analysis (PCA), Gram-Schmidt fusion, wavelet-based fusion and Elhers fusion are used and the results are compared. Of these methods, the best result is obtained by the use of the wavelet-based fusion. Overall, the research indicates that multisource data sets can significantly improve the interpretation and analysis of land cover types.

The aim of this study is to compare the information extracted from high and very high resolution satellite images in terms of information content and reliability to update thematic layers in a geographical information system (GIS). For the extraction of information from the selected remote sensing (RS) data sets, a knowledge-based classification technique based on a rule-based approach has been constructed. The method uses an initial image segmentation procedure based on a Mahalanobis distance classifier (or spectral parameters) as well as the constraints on spectral and spatial thresholds. Overall, the study indicated that unlike the information extracted from high resolution RS images which contains large uncertainty in terms of delineation of individual objects, the information extracted from very high resolution satellite images are very reliable and can be successfully used for update of a GIS layer specifically in urban context.

The aim of this study is to compare the information extracted from high and very high resolution satellite images in terms of information content and reliability to update thematic layers in a geographical information system (GIS). For the extraction of information from the selected remote sensing (RS) data sets, a knowledge-based classification technique based on a rule-based approach has been constructed. The method uses an initial image segmentation procedure based on a Mahalanobis distance classifier (or spectral parameters) as well as the constraints on spectral and spatial thresholds. Overall, the study indicated that unlike the information extracted from high resolution RS images which contains large uncertainty in terms of delineation of individual objects, the information extracted from very high resolution satellite images are very reliable and can be successfully used for update of a GIS layer specifically in urban context.

The aim of this study is to use the original and other ancillary derived features extracted from multitemporal spaceborne interferometric synthetic aperture radar (InSAR) data sets for land cover mapping. For the actual land cover mapping, supervised (statistical maximum likelihood classification) and unsupervised (isodata clustering) classifications have been performed using a) the original InSAR products, b) 14 features and c) the first 3PCs, and the results were compared. Overall, the research indicated that the multitemporal InSAR data sets have a valuable contribution to efficient land cover mapping.