• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1281-1283
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• 2003

By use of RS and GIS, the QuickBird image and geographic map were used for the survey of the foreshore resources of Shanghai. The image was processed and interpreted. The distribution maps of sea dike, foreshore, vegetation, soil, hydraulic structures, landscape, topography, and so on were extracted in manual classification. These data have been integrated into the information management system for the shoreline and foreshore. It plays an important role in the evolvement analysis of the shoreline and foreshore.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.229-233
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• 2007

This study is to test the applicability of QuickBird image for non-point source pollution assessment. SWAT (Soil and Water Assessment Tool) model was adopted and the model was calibrated for a stream watershed of 255.4 $km^2$ Landsat land use data. For model application with QuickBird image, a precise agricultural land use map of 1.16 $km^2$ area located in the upstream watershed was produced by field investigation. The model was run with the combination of land use and soil map scales (1:5,000, 1:25,000 and 1:50,000). The results were compared and analyzed for the contribution of non-point source pollution by the land use scale and contents.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.4
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• pp.37-42
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• 2009

This study extracted Near Infrared (NIR) band using Image Processing Technology (IPT), and calculated Normalized Difference Vegetation Index (NDVI). Aerial photography from Daum portal in combination with high resolution satellite image was employed to improve vegetation sensitivity by extracting NIR band and calculating NDVI with comparison to QuickBird result. The extracted NIR band and NDVI through IPT presented similar distribution pattern. In addition, a regression analysis by land cover character showed high correlation paddy and forest Therefore, this approach could be acceptable to acquire vegetation environment information.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.337-350
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• 2006

The characteristics of neighborhood correlation images for change detection were explored at different spatial resolution scales. Bi-temporal QuickBird datasets of Las Vegas, NV were used for the high spatial resolution image analysis, while bi-temporal Landsat $TM/ETM^{+}$ datasets of Suwon, South Korea were used for the mid spatial resolution analysis. The neighborhood correlation images consisting of three variables (correlation, slope, and intercept) were evaluated and compared between the two scales for change detection. The neighborhood correlation images created using the Landsat datasets resulted in somewhat different patterns from those using the QuickBird high spatial resolution imagery due to several reasons such as the impact of mixed pixels. Then, automated binary change detection was also performed using the single and multiple neighborhood correlation image variables for both spatial resolution image scales.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1197-1199
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• 2003

ROCSAT-2 mission is to daily image over Taiwan and the surrounding area for disaster monitoring, land use, and ocean surveillance during the 5-year mission lifetime. The satellite will be launched in December 2003 into its mission orbit, which is selected as a 14 rev/day repetitive Sun-synchronous orbit descending over (120 deg E, 24 deg N) and 9:45 a.m. over the equator with the minimum eccentricity. National Space Program Office (NSPO) is developing a ROCSAT-2 Image Processing System (IPS), which aims to provide real-time high quality image data for ROCSAT-2 mission. A simulated ROCSAT-2 image, based on Level 1B QuickBird Data, is generated for IPS verification. The test image is comprised of one panchromatic data and four multispectral data. The qualification process consists of four procedures: (a) QuickBird image processing, (b) generation of simulated ROCSAT-2 image in Generic Raw Level Data (GERALD) format, (c) ROCSAT-2 image processing, and (d) geometric error analysis. QuickBird standard photogrammetric parameters of a camera that models the imaging and optical system is used to calculate the latitude and longitude of each line and sample. The backward (inverse model) approach is applied to find the relationship between geodetic coordinate system (latitude, longitude) and image coordinate system (line, sample). The bilinear resampling method is used to generate the test image. Ground control points are used to evaluate the error for data processing. The data processing contains various coordinate system transformations using attitude quaternion and orbit elements. Through the qualification test process, it is verified that the IPS is capable of handling high-resolution image data with the accuracy of Level 2 processing within 500 m.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.227-239
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• 2014

This study aims to evaluate performance of various image fusion methods based on the spectral responses of high-resolution optical satellite sensors such as KOMPSAT-2, QuickBird and WorldView-2. The image fusion methods used in this study are GIHS, GIHSA, GS1 and AIHS. A quality evaluation of each image fusion method was performed with both quantitative and visual analysis. The quantitative analysis was carried out using spectral angle mapper index (SAM), relative global dimensional error (spectral ERGAS) and image quality index (Q4). The results indicates that the GIHSA method is slightly better than other methods for KOMPSAT-2 images. On the other hand, the GS1 method is suitable for Quickbird and WorldView-2 images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.189-197
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• 2012

This research attempted to find possibilities of the practical use utilizing geospatial methods for the balanced promotion of sustainable coastal development and preservation through a case study of Jekyll Island, one of Georgia's barrier islands. In response, this research provided ways for practical use in sustainable development and preservation plans. First this research thoroughly investigated the 1996 master plan of Jekyll Island and tried to recalculate developed and undeveloped areas. Second, new estimations for developed areas were investigated through field survey. Third, this research proposed the use of the satellite images with different levels of spatial resolutions and tested different classification schemes to find possibilities for practical use. For these purposes, first, we classified developed and undeveloped features by manual digitization using an aerial photo image with 0.5m spatial resolution. Second, a Landsat 7 ETM+ and a QuickBird satellite images with mid- and high-levels of spatial resolutions were applied to identify developed and undeveloped areas using both the National Land Cover Data (NLCD) and the Coastal Change Analysis Program (CCAP) classification schemes. Also, GEOBIA (Geographic Object-Based Image Analysis) was conducted to accurately identify developed and undeveloped areas.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.267-272
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• 2008

Tree species-specific estimates with spacebome high-resolution imagery improve estimation of forest biomass which is needed to predict the long term planning for the sustainable forest management(SFM). This paper is a contribution to develop crown distinguishing coniferous species, Pinus densiflora and Pinus koraiensis, from QuickBird imagery. The proposed feature analysis derived from shape parameters and first and second-order statistical texture features of the same test area were compared for the two species separation and delineation. As expected, initial studies have shown that both formfactor and compactness shape parameters provided the successful differentiating method between the pine species within the compartment for single crown identification from spaceborne high resolution imagery. Another result revealed that the selected texture parameters - the mean, variance, angular second moment(ASM) - in the infrared band image could produce good subset combination of texture features for representing detailed tree crown outline.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.303-319
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• 2015

The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.