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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Remote Sensing
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Journal DOI :
The Korean Society of Remote Sensing
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Volume & Issues
Volume 23, Issue 6 - Dec 2007
Volume 23, Issue 5 - Oct 2007
Volume 23, Issue 4 - Aug 2007
Volume 23, Issue 3 - Jun 2007
Volume 23, Issue 2 - Apr 2007
Volume 23, Issue 1 - Feb 2007
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Object-oriented Classification and QuickBird Multi-spectral Imagery in Forest Density Mapping
Jayakumar, S. ; Ramachandran, A. ; Lee, Jung-Bin ; Heo, Joon ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 153~160
Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.
Modeling of Suspended Solids and Sea Surface Salinity in Hong Kong using Aqua/MODIS Satellite Images
Wong, Man-Sing ; Lee, Kwon-Ho ; Kim, Young-Joon ; Nichol, Janet Elizabeth ; Li, Zhangqing ; Emerson, Nick ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 161~169
A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images,
= 0.82 for SS and
= 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.
Design and Implementation of Hyperspectral Image Analysis Tool: HYVIEW
Huan, Nguyen van ; Kim, Ha-Kil ; Kim, Sun-Hwa ; Lee, Kyu-Sung ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 171~179
Hyperspectral images have shown a great potential for the applications in resource management, agriculture, mineral exploration and environmental monitoring. However, due to the large volume of data, processing of hyperspectral images faces some difficulties. This paper introduces the development of an image processing tool (HYVIEW) that is particularly designed for handling hyperspectral image data. Current version of HYVIEW is dealing with efficient algorithms for displaying hyperspectral images, selecting bands to create color composites, and atmospheric correction. Three band-selection schemes for producing color composites are available based on three most popular indexes of OIF, SI and CI. HYVIEW can effectively demonstrate the differences in the results of the three schemes. For the atmospheric correction, HYVIEW utilizes a pre-calculated LUT by which the complex process of correcting atmospheric effects can be performed fast and efficiently.
Comparison of Three Land Cover Classification Algorithms -ISODATA, SMA, and SOM - for the Monitoring of North Korea with MODIS Multi-temporal Data
Kim, Do-Hyung ; Jeong, Seung-Gyu ; Park, Chong-Hwa ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 181~188
The objective of this research was to investigate the optimal land cover classification algorithm for the monitoring of North Korea with MODIS multi-temporal data based on monthly phenological characteristics. Three frequently used land cover classification algorithms, ISODATA1), SMA2), and SOM3) were employed for this study; the land cover categories were forest, grass, agricultural, wetland, barren, built-up, and water body. The outcomes of the study can be summarized as follows. First, the overall classification accuracy of ISODATA, SMA, and SOM was 69.03%, 64.28%, and 73.57%, respectively. Second, ISODATA and SMA resulted in a higher classification accuracy of forest and agricultural categories, but SOM performed better for the built-up area, bare soil, grassland, and water. A possible explanation for this difference would be related to the difference of sensitivity against the vegetation activity. This would be related to the capability of SOM to express all of their values without any loss of data by maintaining the topology between pixels of primitive data after classification, while ISODATA and SMA retain limited amount of data after normalization process. Third, we can conclude that SOM is the best algorithm for monitoring the land cover change of North Korea.
Improving the Quality of Filtered Lidar Data by Local Operations
Seo, Su-Young ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 189~198
Introduction of lidar technology have contributed to a wide range of applications in generating quality surface models. Accordingly, because of the importance of terrain surface models in mapping applications, rigorous studies have been performed to extract ground points from a lidar data point cloud. Although most filters have been shown abilities to extract ground points with their parameters tuned, however, most experiments revealed that there are certain limitations in optimizing filter parameters and the correction of remaining misclassified points is not straightforward. In this study, therefore, a method to improve the quality of filtered lidar data is proposed, which exploits neighboring surface properties arising between immediate neighbors. The method comprises a sequence of procedures which can reduce commission and omission errors. Commission errors occurring in low-rise objects are reduced by utilizing morphological operations. On the other hand, omission errors are reduced by adding missing ground points around step edges. Experimental results show that the qualities of filtered data can be improved considerably by the proposed method.
Building Extraction from Lidar Data and Aerial Imagery using Domain Knowledge about Building Structures
Seo, Su-Young ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 199~209
Traditionally, aerial images have been used as main sources for compiling topographic maps. In recent years, lidar data has been exploited as another type of mapping data. Regarding their performances, aerial imagery has the ability to delineate object boundaries but omits much of these boundaries during feature extraction. Lidar provides direct information about heights of object surfaces but have limitations with respect to boundary localization. Considering the characteristics of the sensors, this paper proposes an approach to extracting buildings from lidar and aerial imagery, which is based on the complementary characteristics of optical and range sensors. For detecting building regions, relationships among elevation contours are represented into directional graphs and searched for the contours corresponding to external boundaries of buildings. For generating building models, a wing model is proposed to assemble roof surface patches into a complete building model. Then, building models are projected and checked with features in aerial images. Experimental results show that the proposed approach provides an efficient and accurate way to extract building models.
A Semi-automated Method to Extract 3D Building Structure
Javzandulam, Tsend-Ayush ; Kim, Tae-Jung ; Kim, Kyung-Ok ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 211~219
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.
An Assessment of Urbanization Using Historic Satellite Photography: Columbus Metropolitan Area, Ohio, 1965
Kim, Kee-Tae ; Kim, Jung-Hwan ; Jayakumar, S. ; Sohn, Hong-Gyoo ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 221~227
We present an analysis of urban development and growth with reconnaissance satellite photographs of Columbus metropolitan area acquired by the Corona program in 1965. A two-dimensional polynomial linear transformation was used to rectify the photos against United State Geological Survey (USGS) Large-scale Digital Line Graph (DLG) data georeferenced to Universal Transverse Mercator (UTM) coordinates. The boundaries of the Columbus metropolitan area were extracted from the rectified Corona image mosaic using a Bayesian approach to image segmentation. The inferred 1965 urban boundaries were compared with 1976 USGS Land Use and Land Cover (LULC) data and boundaries derived from 1988 and 1994 Landsat TM images. The urban area in and around Columbus approximately doubled from 1965 to 1994 (
) along with population growth from 1960 to 1998 (
). Most of the urban expansion results from development of residential units.
Implementation of Satellite Imagery Information System for Korean Meteorological Administration
Chang, Eun-Mi ; Park, Jong-Suh ; Suh, Ae-Sook ;
Korean Journal of Remote Sensing, volume 23, issue 3, 2007, Pages 229~236
Scattered satellite images were collected and converted from TDF to HDF as a standard format. We reviewed all the metadata on the images domestic and abroad and set up the metadata for the meteorological satellite images and naming rules in KMA. The satellite information search system that meteorological satellite images were in service with metadata for public and academic fields was implemented for quick search and download. This system will facilitate satellite images for various academic purposes beyond KMA and management functions of the system make routine workflow to manage satellite images in an ease and standardized way.