• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.539-550
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• 2016

Detection of water bodies in land surface is an essential part of disaster monitoring, such as flood, storm surge, and tsunami, and plays an important role in analyzing spatial and temporal variation of water cycle. In this study, a quantitative comparison of different thresholding-based methods for water body detection and their applicability to Kompsat-5 SAR data were presented. In addition, the effect of speckle filtering on the detection result was analyzed. Furthermore, the variations of threshold values by the proportion of the water body area in the whole image were quantitatively evaluated. In order to improve the binary classification performance, a new water body detection algorithm based on the bimodality test and the majority filtering is presented.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.33-39
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• 2007

Hyperspectral remote sensing data contain plenty of information about objects, which makes object classification more precise. In this paper, we proposed a new spectral similarity measure, called Spectral Mutual Information (SMI) for hyperspectral image classification problem. It is derived from the concept of mutual information arising in information theory and can be used to measure the statistical dependency between spectra. SMI views each pixel spectrum as a random variable and classifies image by measuring the similarity between two spectra form analogy mutual information. The proposed SMI was tested to evaluate its effectiveness. The evaluation was done by comparing the results of preexisting classification method (SAM, SSV). The evaluation results showed the proposed approach has a good potential in the classification of hyperspectral images.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.195-200
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• 2008

The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.369-376
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• 2002

This paper presents a method to enhance the performance of supervised classification by separating the spectral signature of the training data sets for each class. Using clustering technique, a training data set is divided into several subsets which show a pattern of the normal distribution with small value of spectral variances. Then a supervised classification is applied with the divided training data set as training data for the temporary subclasses of the original class. The proposed method is applied to a Landsat TM image of Busan area for the applicability test. The result shows that the proposed method produces better classified results than the conventional statistical classification methods. It is expected that the proposed method will reduce the effort and expense for selecting the training data set for each class in an area which has spectrally homogeneous signature.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.173-183
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• 2014

Field survey research on damages caused by volcanic activities has plenty of difficulties due to human resources, safety and costs issues. Remote sensing application using satellite image is one of very useful tools to overcome those issues. In this study, we monitored the volcanic activities of Sinabung volcano in 2010, which is located in Sumatra island, Indonesia by using Landsat 7 ETM+ satellite images acquired on 17 April, 2009 and 30 July, 2012. We found that the area of pyroclastic flow inundation after 2010 has been tripled roughly, since extracting the pyroclastic flow inundation before and after 2010 eruption from classification. The result from modeling of pyroclastic flow inundation has been compared with the extracted pyroclastic flow inundation from Landsat 7 ETM+ images. As a result, we confirmed that the length of inundation area from the modeling was calculated to 92% accurate, but the width of inundation area was somewhat imprecisely estimated in the volcanic area having the sharp slope and only calculated to 17% accurate.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.147-150
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• 2008

This study refers to develop a semi-automatic extraction of agricultural land use and vegetation information using high resolution satellite images. Data of IKONOS satellite image (May 25 of 2001) and QuickBird satellite image (May 1 of 2006) which resembles with the spatial resolution and spectral characteristics of KOMPSAT3. The precise agricultural land use classification was tried using ISODATA unsupervised classification technique and the result was compared with on-screen digitizing land use accompanying with field investigation. For the extraction of vegetation information, three crops of paddy, com and red pepper were selected and the spectral characteristics were collected during each growing period using ground spectroradiometer. The vegetation indices viz. RVI, NDVI, ARVI, and SAVI for the crops were evaluated. The evaluation process is under development using the ERDAS IMAGINE Spatial Modeler Tool.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.479-481
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• 2003

Large number of coral reefs in Thailand waters make the use of satellite imagery probably the only practical method for their monitoring. This paper reports the result of detecting small shallow-water coral reef by using maximum likelihood classification technique. Combination of blue/green and near-infrared band ratio are used as spectral signatures derived from a Landsat 7 imagery covering western portion of the Gulf of Thailand. Result assessment reveals accuracy significantly over 60 percent. The result is encouraging and would be a basis for further study to realize the full potential and limitation of this technique.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.625-628
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• 2010

This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation based in one dimensional combined neighborhood differences (1D CND). In contrast with the original CND, which is applied with traditional image, 1D CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to these codeword to form another unique value. These values are then grouped to construct the 1D CND feature image where is used in the unsupervised image segmentation. Various experiments using two LANDSAT multispectral images have been performed to evaluate the segmentation and classification accuracy of the proposed method. The result shows that 1D CND feature outperforms the spectral feature, with average classification accuracy of 87.55% whereas that of spectral feature is 55.81%.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.10-13
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• 2004

Hyperspectral data has a great advantage to classify various surface materials that are spectrally similar. In this study, we attempted to classify man-made materials in urban area using Hyperion data. Hyperion imagery of Seoul was initially processed to minimize radiometric distortions caused by sensor and atmosphere. Using color aerial photographs. we defined seven man-made surfaces (concrete, asphalt road. railroad, buildings, roof, soil, shadow) for the classification in Seoul. The hyperspectral data showed the potential to identify those manmade materials that were difficult to be classified by multispectral data. However. the classification of road and buildings was not quite satisfactory due to the relatively low spatial resolution of Hyperion image. Further, the low radiometric quality of Hyperion sensor was another limitation for the application in urban area.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.23-28
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• 2002

Among effective feature extraction methods from the small-patched image set, independent component analysis (ICA) is recently well known stochastic manner to find informative basis images. The ICA simultaneously learns both basis images and independent components using high order statistic manners, because that information underlying between pixels are sensitive to high-order statistic models. The topographic ICA model is adapted in our experiment. This paper deals with an unsupervised classification strategies using learned ICA basis images. The experimental result by proposed classification technique shows superior performance than classic texture analysis techniques for the panchromatic KOMPSAT imagery.