• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1233-1242
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• 2021

In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.1 s.3
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• pp.93-98
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• 1994

This image of Kangseogu in Pusan, is a digital merge of aerial photos by scale of 1/1,200 map. The merge was carried out 2nd affine and bilinear interpolation. It can improve digital classification to help choose training sites and interprete classification results, and improve visual interpretation, as in this case, by adding detailed information to the multispectral TM data.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.81-92
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• 2002

일반적으로 수치사진측량시스템을 통해 도심지역에서 자동으로 DEM을 추출하는 경우 해석도화원도에서 추출하는 DEM에 비하여 정확도가 크게 저하되어 도심지역에 대한 정사투영영상 생성이나 정사투영영상을 이용한 수치지도 제작시 품질저하의 요인이 되고 있다. 따라서 본 연구는 수치사진측량기법을 이용한 도심지역 지형공간정보 생성시 정확도에 영향을 크게 미치는 도심지역 DEM의 정확도를 향상시키는데 목적이 있다. 본 논문의 수행결과, 수치사진측량기법을 이용하여 도심지역에 대한 DEM 추출시 대상지역에 대한 지형분류를 통한 DEM추출방법을 적용하여 도심지역에 대한 DEM의 정확도를 향상시킬 수 있었다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.409-418
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• 2021

As the amount of construction for aerial photography increases, the need for automation of quality inspection is emerging. In this study, an experiment was performed to classify or detect clouds in aerial photos using deep learning techniques. Also, classification and detection were performed by including satellite images in the learning data. As algorithms used in the experiment, GoogLeNet, VGG16, Faster R-CNN and YOLOv3 were applied and the results were compared. In addition, considering the practical limitations of securing erroneous images including clouds in aerial images, we also analyzed whether additional learning of satellite images affects classification and detection accuracy in comparison a training dataset that only contains aerial images. As results, the GoogLeNet and YOLOv3 algorithms showed relatively superior accuracy in cloud classification and detection of aerial images, respectively. GoogLeNet showed producer's accuracy of 83.8% for cloud and YOLOv3 showed producer's accuracy of 84.0% for cloud. And, the addition of satellite image learning data showed that it can be applied as an alternative when there is a lack of aerial image data.

• Journal of the Korean Society for information Management
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• v.8 no.2
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• pp.108-124
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• 1991

정보량이 많은 사진과 같은 화상 정보는 대용량을 갖고 있는 광 디스크에 축적시켜 DB를 구축하고 이는 컴퓨터를 이용해 고속 검색할 수 있다. 사진 DB 구축은 사진의 양, 구 축 목적, 이용 대상, 활용 방법 등에 따라 방법을 달리할 수 있으며 이의 검색 요소에는 촬 영 행위, 촬영 조건, 표제, 주제의 4가지 요소가 있고 그 중 가장 중요시되는 주제 요소에는 감각 정보, 주제 분류, 키워드가 있다.

• Korean journal of applied entomology
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• v.54 no.2
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• pp.63-71
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• 2015

A taxonomic review of Korean Laccophilus Leach is presented. Five species are recognized, three of which [L. hyalinus (De Geer), L. lewisius Sharp and L. minutus ($Linn{\acute{e}}$)] previously recorded in Korea are incorrect identification of L. vagelineatus Zimmermann, L. lewisioides Brancucci and L. difficilis Sharp, respectively. Habitus and SEM photographs, key and diagnoses of the species are provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1175-1181
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• 2004

This paper proposes a system to analyze and summarize the video shots of baseball game TV program into fifteen categories. Our System consists of three modules: feature extraction, Hidden Markov Model (HMM) training, and video shot categorization. Video Shots belongs to the same class are not necessarily similar, so we require that the training set is large enough to include video shot with all possible variations to create a robust Hidden Markov Model. In the experiments, we have illustrated that our system can recognize the 15 different shot classes with a success ratio of 84.72%.

• Korean journal of applied entomology
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• v.58 no.3
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• pp.259-263
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• 2019

Four species, T. michali Radchenko, T. pisarskii Radchenko, T. wui (Wheelr), and T. xanthos Radchenko of the genus Temnothorax were, for the first time, reported in Korea. Authors provide taxonomic information on the morphological characters and photos of species.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.82-82
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• 2020

수리모델링은 유사나 유사량에 따른 하상의 변화를 구현하고 예측하는데 활용하고 있다. 만약 수리모델링을 하천의 생태적 구조나 기능과 연계하여 해석할 수 있다면, 수리학적 모델의 활용 가능성은 무궁무진해 질 수 있다. 본 연구에서는 동일한 시기의 항공사진 영상과 하천단면 자료를 활용한 수리모델링 모의 결과를 이용하여 서식처 구조를 각각 분류하고, 비교 검토하여 수리모델링 모의 결과에서의 서식처 분류 방안을 제안한다. 대상지는 한국의 금강 지류인 갑천 약 2 km 구간이며, 2012년도의 항공사진과 Nays2D모델을 이용한다. 서식처는 여울, 소, 사주 위 웅덩이, 사주부 정수역(backwater) 등으로 구분한다.

• 한국지형공간정보학회:학술대회논문집
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• 1994.10a
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• pp.24-30
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• 1994