• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.4 s.15
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• pp.35-42
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• 2004

Accurate classification of water area is a preliminary step to analyze the flooded area and damages caused by flood. This is essential process for monitoring the region where annually repeating flood is a problem. The accurate estimation of flooded area can ultimately be utilized as a primary source of information for the policy decision. In this paper, flooded areas was classified using 1:25,000 land use map and a RADARSAT image of Ok-Chun and Bo-Eun located in Chung-Book province taken in 12th of August, 1998. Then we analyzed the flood area based on GIS. A RADARSAT image was used to classify the flooded areas with slope theme generated from digital elevation model. In processing on a RADARSAT image, the geometric correction was performed by a backwardgeocoding method based on ephemeris data and one control point for near real time flood area analysis.

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.139-145
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• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.31-41
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• 2016

Atmospheric correction as a preprocessing work should be performed to conduct accurately landcover/landuse classification using hyperspectral imagery. Atmospheric correction on airborne hyperspectral images was conducted and then the effect of atmospheric correction by comparing spectral reflectance characteristics before and after atmospheric correction for a few landuse classes was analyzed. In addition, land cover classification was first conducted respectively by the maximum likelihood method and the spectral angle mapper method after atmospheric correction and then the results were compared. Applying the spectral angle mapper method, the sea water area were able to be classified with the minimum of noise at the threshold angle of 4 arc degree. It is considered that object-based classification method, which take into account of scale, spectral information, shape, texture and so forth comprehensively, is more advantageous than pixel-based classification methods in conducting landcover classification of the coastal area with hyperspectral images in which even the same object represents various spectral characteristics.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.407-415
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• 2006

This study carried out submerged area due to Dam construction in the near future. It includes species classification of plant, survey of community structure, examination of pollutant load and assessment of water quality impact. The vascular plants of this area are listed 224 taxa; 64 families, 168 genera, 193 species, 30 varieties and 1 form. This study area is classified into total 21 communities, most community was consist of grass vegetation. Among the communities, Erigeron annuus ($869,286m^2$, 22%) community was dominant and Erigeron annuus-Avena fatua comminity (16%) was subdominant until May, and then Erigeron canadensis community occupied most area to $1,774,985m^2$ (32%) from May to July. For the evaluation of water quality impact due to submerged macrophyte, nutrient release test was conducted both dead body macrophyte and living body macrophyte. The results of release test show that T-N is not released at dead body macrophyte, but it is released at living body macrophyte, especially living body Artemisia priceps var. orientalis shows 1.436mgN/g. At release test of dead body macrophyte, T-P release rate of Erigeron annuus shows 0.500mgP/g at the top of them and it also shows 0.436mgP/g at Erigeron annuus of living body macrophyte. T-N load of submerged macrophyte shows 0.76% by comparison of total load on watershed and T-P load of that shows 3.61%. In case of removal macrophyte for reduction of pollutant load in submerged area, T-N load of submerged macrophyte changes from 0.76% to 0.15% by comparison of total load on watershed and T-P load of that changes from 3.61% to 0.72%.

• Water for future
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• v.10 no.1
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• pp.95-100
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• 1977

The empirical area-reduction method by W.M. Borland and C.R. Miller and its revised procedure by W.T. Moody were made of fitting the area and storage curves to the Van't Hul distributions. It should be noted that the reservoir is classified into one of the four standard types on the basis of the topographical feature of the reservoir in application of the method. In other words, this method did not take into account several considerafble factors affecting the mode of sediment deposition, but only the shape of the reservoir as a governign factor. This is why the method occasionally creates ambiguity in classification and accordingly leads to unexpected mode of deposition. This paper describes a generating an formula to decide the standard classification of four types Van's Hul distributions, taking into consideration quantitatively sediment-loss percent and capacity-inflow ratio as well as the shape of the reservoirs by multiple regression analysis using the least square method to get a better fit to the design curves. The result is expressed as $Y=-1.95+55.8X_1+0.14X_2+0.12X_3$ in which the the values of Y locate the standard type I through type IV in the range from ten to forty with the interval of ten. The regression analysis was correlated well with the standard errors of estimate of around two except for the case of the type IV. This formula does not give big difference from the Borland's work in general sityation, but it demonstrates acceptable results, giving somewhat precise replys for the specific reservoirs. Its application to the Soyang Lake, one of the largest reservoirs in the country, defined clearly the type II, while the original method located it in the boundary of the type II and type III.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.1-12
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• 2012

The agricultural area was classified into dry and paddy fields in this study using the near-infrared band of Landsat TM to extract land cover classes that need to the application of Expected Mean Concentration (EMC) in nonpoint source works. The accuracy of image classification of the land cover map from Landsat TM image showed 83.61% and 78.41% respectively by comparing with the large and middle scale land cover map of Ministry of Environment. As the result of Soil Conservation Service (SCS) Curve Number (CN) using the land cover map from image classification, Dongbok dam and Dongbok stream basin were analyzed high. Also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of EMC of BOD, TN, TP by basin. And also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of non-point source through coupling with direct runoff. Therefore these basins were selected with the main area for the management of nonpoint source.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.531-548
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• 2012

To identify Cochlodinium polykrikoides red tide from non-red tide water (satellite high chlorophyll waters) in the South Sea of Korea (SSK), we improved a spectral classification method proposed by Son et al.(2011) for the world first Geostationary Ocean Color Imager (GOCI). C. polykrikoides blooms and non-red tide waters were classified based on four different criteria. The first step revealed that the radiance peaks of potential red tide water occurred at 555 and 680 nm (fluorescence peak). The second step separated optically different waters that were influenced by relatively low and high contributions of colored dissolved organic matter (CDOM) (including detritus) to chlorophyll. The third and fourth steps discriminated red tide water from non-red tide water based on the blue-to-green ratio, respectively. After applying the red tide classification, the spectral response of C. polykrikoides red tide water, which is influenced by pigment concentration as well as CDOM (detritus), showed different slopes for the blue and green bands (lower slope at blue bands and higher slope at green bands). The opposite result was found for non-red tide water. This modified spectral classification method for GOCI led to increase user accuracy for C. polykrikoides and non-red tide blooms and provided a more reliable and robust identification of red tides over a wide range of oceanic environments than was possible using chlorophyll a concentration, or proposed red tide detection algorithms. Maps of C. polykrikoides red tide in SSK outlined patches of red tide covering the area near Naro-do and Tongyeong during the end of July and early of August, 2012 and extending into from Wan-do and Geoje-do during the middle of August, 2012.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.611-619
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• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.