• Bulletin of the Korean Chemical Society
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• 제25권3호
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• pp.377-381
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• 2004

Chiral separation of aryalcohols such as 1-phenyl-propanol, 1-phenyl-2-proanol, and 2-phenyl-1-propanol by capillary electrophoresis has been optimized using the overlapping resolution mapping (ORM) scheme. Three critical parameters of the electrophoretic media, i.e. phosphate concentration, sulfated ${\beta}$-cyclodextrin (CD) concentration and pH, were chosen for optimization. The working ranges were initially presumed by 7 preexperiments. Further optimization was carried out by another seven experiments within the narrow working ranges. From the final overlapping resolution mapping all peak pairs, the area of maximum separations were located. Using the conditions of a point in this area, we found that the target compounds were a baseline separated within 30 min. The maximum separation conditions of arylalcohols were a chiral selector concentration of 5.4%, a phosphate concentration of 28 mM, and a pH of 5.0.

• 대한원격탐사학회지
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• 제23권3호
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• pp.211-219
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• 2007

Building extraction is one of the essential issues for 3D city modelling. In recent years, high-resolution satellite imagery has become widely available and it brings new methodology for urban mapping. In this paper, we have developed a semi-automatic algorithm to determine building heights from monoscopic high-resolution satellite data. The algorithm is based on the analysis of the projected shadow and actual shadow of a building. Once two roof comer points are measured manually, the algorithm detects (rectangular) roof boundary automatically. Then it estimates a building height automatically by projecting building shadow onto the image for a given building height, counting overlapping pixels between the projected shadow and actual shadow, and finding the height that maximizes the number of overlapping pixels. Once the height and roof boundary are available, the footprint and a 3D wireframe model of a building can be determined. The proposed algorithm is tested with IKONOS images over Deajeon city and the result is compared with the building height determined by stereo analysis. The accuracy of building height extraction is examined using standard error of estimate.

• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.