• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.35-40
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• 2010

Skyline extraction in mountainous images which has been used for navigation of vehicles or micro unmanned air vehicles is very hard to implement because of the complexity of skyline shapes, occlusions by environments, dfficulties to detect precise edges and noises in an image. In spite of these difficulties, skyline extraction is avery important theme that can be applied to the various fields of unmanned vehicles applications. In this paper, we developed a robust skyline extraction algorithm using two-scale canny edge images, topological information and location of the skyline in an image. Two-scale canny edge images are composed of High Scale Canny edge image that satisfies good localization criterion and Low Scale Canny edge image that satisfies good detection criterion. By applying each image to the proper steps of the algorithm, we could obtain good performance to extract skyline in images under complex environments. The performance of the proposed algorithm is proved by experimental results using various images and compared with an existing method.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

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

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.309-312
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• 2004

In mountainous area with high relief, topography may cause cast shadows due to the blocking of direct solar radiation. Remote sensing images of these landscapes display reduced values of reflectance for shadowed areas compared to non-shadowed areas with similar surface cover characteristics. A variety of approaches are possible, though a common step in various active approaches is first to delineate the shadows using automated algorithm and digital surface model (or digital elevation model). This articles demonstrates a common confusion caused by cast shadows

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.251-254
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• 2008

The German radar satellite TerraSAR was launched in 2007. In this study, interferogram is generated using TerraSAR-X data and DEM (Digital Elevation Model). Coregistration procedures used with SAR images (i.e. master and slave) in traditional method results in serious errors for high resolution TerraSARX data because of the mutual shift of the master and slave images due to topography. This error becomes more serious in mountainous areas in which the coherence between interferometric pairs is relatively low. Here we processed a geometric coregistration with DEM exploiting height information. Through the method, interferometry processing is fulfilled to generate a qualified interferogram and coherence is improved. This approach will help high resolution X-band SAR interferometry in mountainous area.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.315-324
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• 2013

Spatial information of snow cover and depth distribution is a key component for snowmelt runoff modeling. Wide snow cover areas can be extracted from NOAA AVHRR or Terra MODIS satellite images. In this study eight sets of annual snow cover data (1997-2006) in two mountainous watersheds (A: Chungju-Dam and B: Soyanggang-Dam) were extracted using NOAA AVHRR images. The distribution of snow depth within the Snow Cover Area (SCA) was generated using snowfall data from ground meteorological observation stations. Snow depletion characteristics for the two watersheds were analyzed snow distribution time series data. The decreased pattern of SCA can be expressed as a logarithmic function; the determination coefficients were 0.62 and 0.68 for the A and B watersheds, respectively. The SCA decreased over 70% within 10 days from the time of maximum SCA.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1409-1411
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• 2003

The application of simulated KOMPSAT-2 imagery to monitor graveyards is to be developed. Positions calculated from image were compared with those obtained from Geographic Positioning System. With 24 checkpoints, the position of graveyards showed within 5-meter range. Unsupervised classification, supervised classification, and objected-orientation classification algorithms were used to extract the graveyard. Unsupervised classification with masking processes based on National topographic data gives the best result. The graveyards were categorized with four types in field studies while the two types of graveyards were shown in descriptive statistics. Cluster Analysis and discriminant analysis showed the consistency with two types of tombs. It was hard to get a specific spectral signature of graveyards, as they are covered with grasses at different levels and shaded from the surrounding trees. The slopes and aspects of location of graveyards did not make any difference in the spectral signatures. This study gives the basic spectral characteristics for further development of objected-oriented classification algorithms and plausibility of KOMPSAT-2 images for management of mountainous areas in the aspect of position accuracy and classification accuracy.

• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.4
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• pp.71-80
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• 2007

The purpose of this study is to suggest the criteria for the damaged mountainous landscape based on the shape, location and ratio of damaged landscape. For the study, the preference and landscape adjectives were analyzed on visual images and simulations. The variables for analysis were the amount of the damaged ratio(10%, 30%, 50%), the location of the damage (upper, middle, lower) and the various forms of the damage(spot, line, area). According to the results of this study, in accordance with the amount of damage, the visual preference recorded its lowest with the a rate of 50%. As for the location of the damage, the lower-ridge of the mountain showed the highest preference, and the upper-ridge was recorded as the lowest. The linear damage type showed the highest preference. On the other hand, the spotted damage type showed lowest. The results indicate that the visual preference increases when there is a lower ratio of damage, as the damage locates at the lower-ridge, and also when there is a presence of linear formation development. The group of linear formation-the lower ridge-10% showed the highest preference, and the group of linear formation-the mid ridge-50% was the lowest with the results of 3-way ANOVA. The group of linear formation-lower ridge-10% in particular had virtually no differences of visual preference when it was compared with the original scene. The damage with the spotted formation, on the mid-upper location and the high ratio of damage were analyzed as factors that give negative influence on the mountainous landscape. The main features of mountainous landscape were reduced into two factors, 'total estimation' and 'spatial scale' by the factor analysis with total variance of 65.96%.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.297-308
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• 2001

We propose an algorithm which can reconstruct the 3D information from geographical information. The conventional techniques, the triangular patches and the Random Fractal Midpoint Displacement (RFMD) method, etc., have often been used to reconstruct natural images. While the RFMD method using Gaussian distribution obtains good results for the symmetric images, it is not reliable on asymmetric images immanent in the nature. Our proposed algorithm employs neural networks for the RFMD method to present the asymmetrical images. By using a neural network for reconstructing the 3D images, we can utilize statistical characteristics of irregular data. We show that our algorithm has a better performance than others by the point of view on the similarity evaluation. And, it seems that our method is more efficient for the mountainous topography which is more rough and irregular.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1071-1081
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• 2021

Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.