• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.1-14
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• 2020

In this study, we 1) identified the damaged areas along the south limit line (SLL) of the demilitarized zone (DMZ) by the military's 'DMZ barren land campaign', and 2) categorized the identified damaged areas into a few ecological types. Using high-resolution satellite images, we delineated the total damaged areas to be 1,183.2 ha, which accounted for 50.1% of the 100-m northern buffer regions from the SLL. Of the total damaged areas, 16% were severely damaged, i.e., they had been damaged until recently and so remained barren without vegetation cover. In other areas, the levels of damage were either moderate (59.9%) or slight (24.1%), due to natural succession that turned those areas to grassland or forest. Using satellite image-derived land cover maps and climatic and topographic data, we categorized the damaged areas into seven types: lowland grassland (19.8%), western lowland forest (21.4%), low-altitude forest (25.5%), mid-altitude forest (18.4%), high-altitude forest (6.8%), vicinity in east coast (7.9%), and waterbody (0.2%). These types can be used to identify proper measures to restore ecosystems in the DMZ for now and after Korean reunification.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1469-1478
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• 2018

Earthquakes and volcanic eruptions are disaster that causes billions of dollars in property damage and the loss of human life. Therefore, it is required to effectively monitor earthquakes and volcanoes. With the increase of satellite data, researches on earthquake and volcano using satellite imagery has been improved. Satellite images can be divided into three types i.e. optical, thermal, Synthetic Aperture Radar (SAR) and each image has different characteristics. In this article, we summarized its advantages and disadvantages of each type of satellite image. Moreover, we investigated the previous researches about earthquake and volcano using satellite images. Finally, we suggest application method to respond earthquake and volcano disaster using satellite images.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.4
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• pp.1-10
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• 2000

Satellite remote sensing images have much more information compared to a paper map. But these images are generally handled as particular image format gained from optical sensor, and must be processed and analyzed by computer with high priced digital image processing system. For the extraction of digital elevation model(DEM) from satellite image, we used the overlay image by SPOT-3 of Chuncheon area at the Kangwon province. According to the image condition, the precious geometric correction, the bundle adjustment for ortho-image generation and the stereo image mapping by several technical approaches were processed. So that we developed the methods of automatic DEM extraction and efficient stereo image map generation which can improve the digital image processing steps. Also, we applied the multiple direction birdeye view image for modeling and analysis using the remotely sensed overlay images with high resolution.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.75-87
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• 2011

This paper presents a method for region-based building extraction from high resolution satellite images(HRSI) using integrated information of spectral and color invariant features without user intervention such as selecting training data sets. The purpose of this study is also to evaluate the effectiveness of the proposed method by applying to IKONOS and QuickBird images. Firstly, the image is segmented by the MSRG method. The vegetation and shadow regions are automatically detected and masked to facilitate the building extraction. Secondly, the region merging is performed for the masked image, which the integrated information of the spectral and color invariant features is used. Finally, the building regions are extracted using the shape feature for the merged regions. The boundaries of the extracted buildings are simplified using the generalization techniques to improve the completeness of the building extraction. The experimental results showed more than 80% accuracy for two study areas and the visually satisfactory results obtained. In conclusion, the proposed method has shown great potential for the building extraction from HRSI.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.107-115
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• 2008

Recently, a considerable number of studies have been conducted on the high resolution imagery showing the boundaries of objects clearly. When urban areas are analyzed in detail using the high resolution imagery, the size of analyzed zone is apt to be decided arbitrarily. Sufficient prior information about study area makes the decision of analysis zone possible; otherwise, it is difficult to determine the optimized analysis zone using only satellite imagery. In this study, the variograms of artificial simple images are analyzed before applying to the real satellite images. As a result of the analysis of simple images, the sill has an effect on the density of objects and also the size of objects and spacing influence the range. The variograms of real satellite images are analyzed with reference to the result of model test and are applied to determining the optimized analysis zone. This study shows that variogram can be applied to determining effectively the optimized analysis zone in case of no prior information on study area; moreover it will be expected to be used for an index to express the characteristics of urban imagery as well as conventional kriging and simulation.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.295-301
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• 2004

Satellite captures images periodically and economically over the area wider than aerial photographs, and reconnaissance to unapproachable area. For these advantages, mapping using high resolution satellite image has high potentials of marketability and development. Therefore, utilization of satellite image in mapping and GIS is expected to be growing and research on describable feature, positional accuracy and, possible mapping scale is urgently needed. This research presented that Quick Bird orthoimage could be used to update digital map on a scale of 1:5,000. Quick Bird image was corrected geometrically based on ground control points. DEM was generated using height data of digital topographic map. The orthoimge was produced by digital differential rectification based on DEM which was generated using height data of digital topographic map(scale 1;5,000 and 1;1,000). When the digital topographic map was overlaid with the orthoimage, it was very easy to find changed region or new features builded after the map compiled.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.306-309
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• 2004

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the Geographic Information System (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat TM (Thermatic Mapper) satellite images; and the vegetation index value from SPOT HRV (High Resolution Visible) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors employing the probability-frequency ratio method. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, land 'cover had relatively positive effects, and lithology had relatively negative effects on the landslide susceptibility maps in the study area. In addition, the landslide susceptibility maps using the all factors showed the relatively good results.