• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.35-42
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• 2020

In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector's visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.451-457
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• 2014

Recently, the land degradation and pasture erosion in Tuv, located around Ulaanbaatar of Mongolia, have been increasing sharply due to escalating developments of mining sectors, well as the density of populations. Because of that, we have chosen the urban and mining area of Tuv for our study target. During the study, the temporal changes of land cover in Tuv, Mongolia were observed by the Landsat TM/+ETM satellite images from 2001 to 2009 that provided the fundamental dataset to apply NDVI and K-Mean algorithm of Unsupervised Classification and Maximum likelihood classification(MLC) of Supervised Classification in order to conclude in land cover change analyzation. The result of our study implies that the growth of mining area, the climate change, and the density of population led the land degradation to desertification.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1075-1087
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• 2017

In this study, we tried to utilize results of the change detection analysis for satellite images as the basis for updating the land cover map. The Sequential Spectral Change Vector Analysis ($S^2CVA$) was applied to multi-temporal multispectral satellite imagery in order to extract changed areas, efficiently. Especially, we minimized the false alarm rate of unsupervised change detection due to the seasonal variation using the direction information in $S^2CVA$. The binary image, which is the result of unsupervised change detection, was integrated with the existing land cover map using the zonal statistics. And then, object-based analysis was performed to determine the changed area. In the experiment using PlanetScope data and the land cover map of the Ministry of Environment, the change areas within the existing land cover map could be detected efficiently.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.777-786
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• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.160-168
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• 2007

For developing a text classifier using supervised learning, a manually labeled corpus of large size is required. However, it takes a lot of time and human effort. Recently a research paradigm was proposed to use a raw corpus and a small amount of seed information instead of manually labeled corpus. In this paper we introduce an unsupervised learning method that makes it possible to achieve better performance than other related works. The characteristics of our approach is that average mutual information is used to learn representative words and their weights and then update of the weights is done using a technique inspired by the works in information retrieval. By iterating this teaming process it was shown that a high performance system can be developed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.487-496
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• 2011

This study is aimed to perform more reliable unsupervised change detection through the re-extraction of the changed pixels which were extracted with global thresholding by applying buffer zone concept. First, three buffer zone was divided on the basis of the thresholding value which was determined using range average and the maximum distance point from a straight line. We re-extracted the changed pixels by performing unsupervised classification for buffer zone II which consists of changed pixels and unchanged pixels. The proposed method was implemented in Hyperion hyperspectral images and evaluated comparing to the existing global thresholding method. The experimental results demonstrated that the proposed method performed more accuracy change detection for vegetation area even if extracted slightly more changed pixels.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.11-18
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• 2014

In This paper, we propose the unsupervised learning and fuzzy logic-based coupled data classification method base on ART. The unsupervised learning-based data classification helps improve the grouping technique, but decreases the processing efficiency. However, the data classification requires the decision technique to induce high success rate of data classification with optimal threshold. Therefore it is also necessary to solve the uncertainty of the threshold decision. The proposed method deduces the optimal threshold with the designing of fuzzy parameter and rules. In order to evaluate the proposed method, we design the simulation model with the GPCR(G protein coupled receptor) data in cloud computing environment. Simulation results verify the efficiency of our method with the high recognition rate and low processing time.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1067-1076
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• 2020

In this paper, an unsupervised saliency guided change detection method using UAV and aerial imagery is proposed. Regions that are more different from other areas are salient, which make them more distinct. The existence of the substantial difference between two images makes saliency proper for guiding the change detection process. Change Vector Analysis (CVA), which has the capability of extracting of overall magnitude and direction of change from multi-spectral and temporal remote sensing data, is used for generating an initial difference image. Combined with an unsupervised CVA and the saliency, Principal Component Analysis(PCA), which is possible to implemented as the guide for change detection method, is proposed for UAV and aerial images. By implementing the saliency generation on the difference map extracted via the CVA, potentially changed areas obtained, and by thresholding the saliency map, most of the interest areas correctly extracted. Finally, the PCA method is implemented to extract features, and K-means clustering is applied to detect changed and unchanged map on the extracted areas. This proposed method is applied to the image sets over the flooded and typhoon-damaged area and is resulted in 95 percent better than the PCA approach compared with manually extracted ground truth for all the data sets. Finally, we compared our approach with the PCA K-means method to show the effectiveness of the method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.

• Journal of Environmental Impact Assessment
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• v.11 no.4
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• pp.247-258
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• 2002

This study aimed at verifying the grain-sized distribution of surface deposits in a tidal flat using multi-spectral Landsat TM. In this study, we employed the grain-sized analysis, PCA and unsupervised classification techniques for analyzing the distribution of deposits. As a result in this study, the unsupervised classification method using PCA image was found to be most useful in classifying tidal flat deposits using satellite data. This method is considerably effective in analyzing not only the aspects of distribution in terms of accumulated deposits and erosion, but also the changes in seaside topography and shoreline. The grain-sized distribution analysis indicates that the mud flat inside the Cheonsu-bay tidal flat is distributed, the mixed flat located in the middle, and the sand flat distributed near the sea. The sand flat is dominant around the southern part of Seomot isle and its beach. On the other hand, the mud and mixed flat is dominant on the western part. Likewise, the western coast of Seomot isle and its beach is significantly affected by waves facing the offshore. However, the eastern side of the bay could be a site for the evolution of tidal flat made of fine materials where it is less affected by ocean waves. These results show that multi-spectral satellite data are effective for the classification of distribution materials and environmental impact assessment and continuous monitoring. In particular, the research on environmental deposits can provide important decision-supporting information for decision-making on seaside development, by analyzing the progress of deposits and environmental changes.