• Water for future
• /
• v.6 no.1
• /
• pp.55-58
• /
• 1973

• Korean Journal of Geomatics
• /
• v.3 no.1
• /
• pp.33-41
• /
• 2003

Every year, several typhoons hit the Korean peninsula and cause severe damage. For the prevention and accurate estimation of these damages, real time or almost real time flood information is essential. Because of weather conditions, images taken by optic sensors or LIDAR are sometimes not appropriate for an accurate estimation of water areas during typhoon. In this case SAR (Synthetic Aperture Radar) images which are independent of weather condition can be useful for the estimation of flood areas. To get detailed information about floods from satellite imagery, accurate classification of water areas is the most important step. A commonly- and widely-used classification methods is the ML(Maximum Likelihood) method which assumes that the distribution of brightness values of the images follows a Gaussian distribution. The distribution of brightness values of the SAR image, however, usually does not follow a Gaussian distribution. For this reason, in this study the ANN (Artificial Neural Networks) method independent of the statistical characteristics of images is applied to the SAR imagery. RADARS A TSAR images are primarily used for extraction of water areas, and DEM (Digital Elevation Model) is used as supplementary data to evaluate the ground undulation effect. Water areas are also extracted from KOMPSAT image achieved by optic sensors for comparison purpose. Both ANN and ML methods are applied to flat and mountainous areas to extract water areas. The estimated areas from satellite imagery are compared with those of manually extracted results. As a result, the ANN classifier performs better than the ML method when only the SAR image was used as input data, except for mountainous areas. When DEM was used as supplementary data for classification of SAR images, there was a 5.64% accuracy improvement for mountainous area, and a similar result of 0.24% accuracy improvement for flat areas using artificial neural networks.

• Journal of the Korean Geographical Society
• /
• v.43 no.5
• /
• pp.761-774
• /
• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.433-438
• /
• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.

• Journal of Korean Society of Water and Wastewater
• /
• v.29 no.6
• /
• pp.651-657
• /
• 2015

Recently, the need for asset management(AM) plan introduction to reduce increasing O&M cost with aging water facilities is on the rise. Therefore, asset inventory classification is necessary as the first step for AM plan construction. In this study, all assets of YW water treatment plant(WTP) were classified as 5 steps. In addition, specific code name was given to each asset which can increase compatibility in constructing the AM programs among WTPs. In the future, codes for attribute and status of asset will be allocated, which can facilitate proper AM operation.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1239-1241
• /
• 2003

This paper presents the results of Landsat-TM imagery applications for detecting spatial variations of the water environments in the Saemankeum (STLR) project areas. The simulated tidal flow patterns from a two -dimensional hydro - dynamic model and water quality data from STRL project were used for relationships with the satellite data. Unsupervised classification of the tidal water body reflects the overall flow patterns at a flooding tide. Regressive equations for water quality parameters were derived and used for supervised classifications. The results were found to be useful to synoptically evaluate the water environments during the construction stages of the STLR project.

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

• Journal of Wetlands Research
• /
• v.13 no.2
• /
• pp.275-289
• /
• 2011

We investigated the hydrological and geomorphological characteristics of small irrigation ponds in civilian control zone of Paju city in Korea. Among 85 small irrigation ponds, water level of 52 ponds changed seasonally and that of 33 was constant. Water sources of 12 ponds were surface water, 29 surface water and ground water, and 44 ground water. 4 ponds locate in the edges of forests, 33 in flat-lands, and 48 in valleys. Water in 45 ponds was exchanged with paddy fields and 40 ponds were isolated from paddy fields. Endangered or endemic species were inhabited in 26 ponds, which have ground water as water source and constant water level. Based on these characteristics, we suggested 4 types of small irrigation ponds: spring, water exchanging, stagnant/spring, stagnant water. This classification system will help ecosystem managers to investigate ponds systematically and manage them based on pond type.

• Ecology and Resilient Infrastructure
• /
• v.9 no.4
• /
• pp.218-227
• /
• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.204-206
• /
• 2003

In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But, the accuracy of traditional methods based on pixel-based classification is not high in general. In this study, object oriented classification based on image segmentation is used to classify Landsat images. A necessary prerequisite for object oriented image classification is successful image segmentation. Object oriented image classification, which is based on fuzzy logic, allows the integration of a broad spectrum of different object features, such as spectral values , shape and texture. Landsat images are divided into urban, agriculture, forest, grassland, wetland, barren and water in sochon-gun, Chungcheongnam-do using object oriented classification algorithms in this paper. Preliminary results will help to perform an automatic image classification in the future.