• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.210-213
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• 2006

이 연구에서는 다중시기 Landsat 영상을 이용하여 강원도 일대 임상의 변화를 살펴보고 상록수의 영급을 구분하는 알고리즘을 개발하여 적용하였다. 1980년대에서 현재까지 축적된 Landsat-5와 Landsat-7영상 중에서, 대부분 지역에 활잡목 및 활엽수가 낙엽이 지고 눈이 아직 쌓이지 않을 시기인 11월에 촬영된 영상만을 이용하였다. 각 영상에서 양지바른 상록수, 활엽수, 그늘진 지역, 도시 및 바다 등을 클래스로 지정하여 감돌분류를 하였다. 분류 결과에서 양지바른 상록수만 추출하여 5개의 영상을 이진 분류체계로 조합한 후 임상의 시기적 변화 양상을 관찰한 결과, 강원대 연습림의 조림 기록 및 현황도와 상당히 일치함을 확인하였으며, Path 115, Row 34에 해당하는 강원도 일대로 연구지역을 확대하였다. 향후 Kompsat-2를 비롯한 고해상도 11월 영상이 지속적으로 촬영된다면, 이 연구에서 개발된 이진 분류체계 방법을 통하여 산림변화의 모니터링을 보다 용이하고 효율적으로 할 수 있을 것으로 기대된다.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.21-27
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• 2006

Among various image fusion methods, intensity-hue-saturation(IHS) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, IHS can yield satisfactory 'spatial' enhancement but may introduce 'spectral' distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To solve this problem a fast IHS fusion technique with spectral adjustment is presented. The experimental results demonstrate that the proposed approach can provide better performance than the conventional IHS method, in both processing speed and image quality.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.81-88
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• 2015

Various satellite sensors having ranges of the visible, infrared, and thermal wavelengths have been launched due to the improvement of hardware technologies of satellite sensors development. According to the development of satellite sensors with various wavelength ranges, the fusion and integration of multisensor images are proceeded. Image matching process is an essential step for the application of multisensor images. Some algorithms, such as SIFT and SURF, have been proposed to co-register satellite images. However, when the existing algorithms are applied to extract matching points between optical and thermal images, high accuracy of co-registration might not be guaranteed because these images have difference spectral and spatial characteristics. In this paper, location of control points in a reference image is extracted by SURF, and then, location of their corresponding pairs is estimated from the correlation of the local similarity. In the case of local similarity, phase correlation method, which is based on fourier transformation, is applied. In the experiments by simulated, Landsat-8, and ASTER datasets, the proposed algorithm could extract reliable matching points compared to the existing SURF-based method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.87-94
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• 2005

3D image mosaicking is useful for 3D visualization of the roadside scene of urban area by projecting 2D images to the 3D planes. When a sequence of images are filmed from a side-looking video camera passing long distance areas, the ghost effect in which same objects appear repeatively occurs. To suppress such ghost effect, the long distance range areas are detected by using the distance between the image frame and the 3D coordinate of tracked optical flows. The ghost effects are suppressed by projecting the part of image frames onto 3D multiple planes utilizing vectors passing the focal point of frames and a virtual focal point. The virtual focal point is calculated by utilizing the first and last frames of the long distance range areas. We demonstrate algorithm that creates efficient 3D Panoramic mosaics without the ghost effect at the long distance area.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.44-50
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• 2011

We propose a multi-class AdaBoost algorithm for en efficient classification of multi-class data in this paper. Traditional AdaBoost algorithm is basically a binary classifier and it has limitations when applied to multi-class data problems even though multi-class versions are available. In order to overcome the problems on the AdaBoost algorithm for multi-class classification problems, we devise an AdaBoost architecture with a training algorithm that utilizes multi-class classifiers for its weak classifiers instead of series of binary classifiers. Experiments on a image classification problem using collected Caltech Image Database are preformed. The results show that the proposed AdaBoost architecture can reduce its training time while maintaining its classification accuracy competitive when compared to Adaboost.M2.

• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.786-803
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• 2004

These days, interests on land cover classification using not only multi-sensor data but also thematic GIS information, are increasing. Often, although we have useful GIS information for the classification, the traditional classification method like maximum likelihood estimation technique (MLE) does not allow us to use the information due to the fact that the MLE and the existing computer programs cannot handle GIS data properly. We proposed a new method for updating the image classification using multi-spectral and multi-temporal images. In this study, we have simultaneously extended the MLE to accommodate both multi-spectral images data and land cover data for land cover classification. In addition to the extended MLE method, we also have extended the empirical likelihood ratio estimation technique (LRE), which is one of non-parametric techniques, to handle simultaneously both multi-spectral images data and land cover data. The proposed procedures were evaluated using land cover map based on Landsat ETM+ images in the Anmyeon-do area in South Korea. As a result, the proposed methods showed considerable improvements in classification accuracy when compared with other single-spectral data. Improved classification images showed that the overall accuracy indicated an improvement in classification accuracy of $6.2\%$ when using MLE, and $9.2\%$ for the LRE, respectively. The case study also showed that the proposed methods enable the extraction of the area with land cover change. In conclusion, land cover classification produced through the combination of various GIS spatial data and multi-spectral images will be useful to involve complementary data to make more accurate decisions.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.37-48
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• 2012

Image registration is a process to establish the spatial correspondence between the images of same scene, which are acquired at different view points, at different times, or by different sensors. In this paper, we propose an effective registration method for images acquired by multi-sensors, such as EO (electro-optic) and IR (infrared) sensors. Image registration is achieved by extracting features and finding the correspondence between features in each input images. In the recent research, the multi-sensor image registration method that finds corresponding features by exploiting NMI (Normalized Mutual Information) was proposed. Conventional NMI-based image registration methods assume that the statistical correlation between two images should be global, however images from EO and IR sensors often cannot satisfy this assumption. Therefore the registration performance of conventional method may not be sufficient for some practical applications because of the low accuracy of corresponding feature points. The proposed method improves the accuracy of corresponding feature points by combining the gradient orientation as spatial information along with NMI attributes and provides more accurate and robust registration performance. Representative experimental results prove the effectiveness of the proposed method.

• Journal of KIISE
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• v.42 no.8
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• pp.990-997
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• 2015

Panoramic image creation has been extensively studied. Existing methods use customized hardware, or apply post-processing methods to seamlessly stitch images. These result in an increase in either cost or complexity. In addition, images can only be stitched under certain conditions such as existence of characteristic points of the images. This paper proposes a low cost and easy-to-use system that produces realtime panoramic video. We use an off-the-shelf embedded platform to capture multiple images, and these are then transmitted to a server in a compressed format to be merged into a single panoramic video. Finally, we analyze the performance of the implemented system by measuring time to successfully create the panoramic image.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.767-776
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• 2020

GcGAN is a deep learning model to translate styles between images under geometric consistency constraint. However, GcGAN has a disadvantage that it does not properly maintain detailed content of an image, since it preserves the content of the image through limited geometric transformation such as rotation or flip. Therefore, in this study, we propose a new image-to-image translation method, MSGcGAN(Multi-Scale GcGAN), which improves this disadvantage. MSGcGAN, an extended model of GcGAN, performs style translation between images in a direction to reduce semantic distortion of images and maintain detailed content by learning multi-scale images simultaneously and extracting scale-invariant features. The experimental results showed that MSGcGAN was better than GcGAN in both quantitative and qualitative aspects, and it translated the style more naturally while maintaining the overall content of the image.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.167-170
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• 2007

연안 김 양식장의 효과적 관리를 위해서는 실제 시설량의 조사가 펼요하며 인공위성을 이용한 방법이 가장 효과적이다. 본 연구에서는 스펙트로미터에 의한 해수 및 김 양식장 시설에 대한 광 측정을 통하여 파장별 특성을 조사하였다 10m의 해상도를 갖고 있는 SPOT-5 다중분광영상을 사용하였으며，김 양식장의 자동탐지알고리듬의 개발을 위하여 경기도 화성시 제부도 남방해역에 대한 2005년도 영상을 사용하였다. 김 양식장을 추출하기 위하여 우선 3밴드 영상의 분광특성을 이용한 밴드차(Band difference) 영상을 작성하여，두 가지 방법(형태학적 처리기법 및 Canny 에지 탐지기법)으로 처리를 한 후，두 결과를 합성하여 라벨령함으로써 탐지율을 극대화하였다 양식장 시설 현황 조사 결과는，정부에서 전체 생산량을 조절할 수 있게 하며，양식업자가 좋은 수확을 달성하는데 도움이 될 수 있을 것이다.