• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.

• Journal of The Geomorphological Association of Korea
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• v.18 no.1
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• pp.41-56
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• 2011

This study analyzes the change in land-cover based on satellite imagery to draw up land-cover map in the future, and estimates the change in land category using statistical data of the land category. To estimate land category, this study applied the double exponentially smoothing method. The result of the land cover classification according to year using satellite imagery showed that the type with the largest increase in area of land cover change in the cities of Cheonan and Asan was artificial structure, followed by water, grass field and bare land. However forest, paddy, marsh and dry field were reduced. Further, the result of the time-series analysis of the land category was found to be similar to the result of the land cover classification using satellite imagery. Especially, the result of the estimation of the land category change using the double exponentially smoothing method showed that paddy, dry field, forest and marsh are anticipated to consistently decrease in area from 2010 to 2100, whereas artificial structure, water, bare land and grass field are anticipated to consistently increase. Such results can be utilized as basic data to estimate the change in land cover according to climate change in order to prepare climate change response strategies.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.203-203
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• 2011

최근 들어 기후변화에 따른 강우패턴이 바뀌고 각종 하천개발이나 토목공사, 농경지, 경작지 등의 객토 등으로 인해 매년 탁수의 발생이 크게 증가하고 있는 추세이다. 특히 여름철 집중강우의 영향에 따라 상류지역 하천에서 발생하는 부유물질은 호수로 유입되어 장기간 체류하며 심각한 오염원으로서 수중생태계에 치명적인 영향을 주고 있다. 또한 하천과 호수의 상류지역의 농경지나 경작지에서 발생된 부유물질에는 과도한 비료의 사용으로 입자표면에 많은 인을 포함하고 있어 호수 수질악화 및 부영양화의 직접적 요인이 되고 있다. 이에 따라 세계 각국에서는 부유물질은 오염원뿐 아니라 생태계에 영향을 주는 인자로서 엄격히 규제하고 있으며, 특히 농업지역이 많은 하천에 대해서는 유역전체를 대상으로 부유물질에 대한 총량관리를 적용하고 있다. 그러나 우리나라의 경우 하천 수질기준 1급수의 부유물질 농도는 25 mg/l 로서 이는 선진국과 유사한 기준이나 실질적으로 규제가 어려운 실정이다. 수환경에서의 부유물질이란 수체 내 존재하는 유기성, 무기성 물질로써 입자 지름이 2mm 이하의 물에 용해되지 않는 물질을 말하는 것으로, 물의 탁도를 유발시키는 원인이 되며 빛을 차단하여 수생태계에 악영향을 초래한다. 국내 132개 하천을 대상으로 부유물질의 농도와 어류의 종 다양성간 상관성을 조사한 결과, 부유물질의 농도가 15 ~ 20 mg/l 이상에서 종 다양도는 1.0 이하로 급감하는 경향을 보였다(최재석 등, 2004). 한편, 대청호는 1975년부터 1980년에 걸쳐 건립된 저수 면적 $72.8km^2$, 저수량 15억톤의 인공호수로 우리나라 3번째 규모의 인공호수이다. 특히, 대전 및 청주지역의 식수는 물론, 생활용수 및 공업용수를 공급하는 중요한 수자원으로서 부유물질에 대한 모니터링 및 관리가 시급하나 저수 용량이 크고 체류시간이 길어 여름철 부영양화가 매년 반복되고 있다. 따라서 본 연구에서는 부유물질의 농도 변화에 따른 분광반사 특성을 조사하고, 이를 대청호의 Landsat 위성영상에 적용하여 대청호 내 부유물질의 농도변화를 추정하였다. 이와 함께 부유물질 농도 변화에 따른 탁수 환경 모니터링에 원격탐사 기법이 효과적임을 제시하고자 하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.1 s.19
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• pp.101-108
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• 2002

The studies using satellite data have been progressed in many area. Especially, Landsat data have been widely used due to its wide coverage. To establish a plan for preserving fishing and environment preservation, measurement of sea temperature is important. The measurement of the sea temperature was made on the ship. However, the measurement on the ship could not provide sufficient information due to the poor timing with relatively longer measurement, and point-based data acquisition. Thus remote sensing technique is required because satellite image data offer more wide coverage in sea temperature monitoring. The purpose of this paper was to study on the sea temperature monitoring with thermal band information of Landsat ETM+. From this study, sea temperature map of Cheju island has been made efficiently.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1965-1974
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• 2021

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.61-70
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• 1995

In this paper, the algorithm to extract the height of flat-rooftop structures in stereo aerial image is suggested with an assumption that location, orientation, focal length, and field of view of a camera are known. It can be adapted to stereo aerial or satellite images. For performing feature-based stereo matching, the line segments suitable to describe the shape of general buildings are chosen as the feature. This paper is composed of three categories;the first step is to extract edges of structures with the polygon extraction algorithm which utilizes the edge following method, the second step is to perform the line segment matching with the camera information, and the last step is to calculate the location of each matched line and to estimate heights. The stereo images used in experiments are not real but synthetic ones. The experiment shows good results.

• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.198-209
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• 1996

In this paper, we present an image compression algorithm that is capable of significantly reducing the vast mount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet trans-form to remove the spatial redundancy. The transformed images are than encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with KITSAT-1 image as well as the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by peak signal to noise ratio (PSNR) and classification capability.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.3-13
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• 2006

Extraction of man-made objects from high resolution satellite images has been studied by many researchers. In order to reconstruct accurate 3D building structures most of previous approaches assumed 3D information obtained by stereo analysis. For this, they need the process of sensor modeling, etc. We argue that a single image itself contains many clues of 3D information. The algorithm we propose projects virtual shadow on the image. When the shadow matches against the actual shadow, the height of a building can be determined. If the height of a building is determined, the algorithm draws vertical lines of sides of the building onto the building in the image. Then the roof boundary moves along vertical lines and the footprint of the building is extracted. The algorithm proposed can use the shadow cast onto the ground surface and onto facades of another building. This study compared the building heights determined by the algorithm proposed and those calculated by stereo analysis. As the results of verification, root mean square errors of building heights were about 1.5m.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.274-274
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• 2021

토양수분은 가뭄, 홍수, 산불 및 산사태 등 자연재해 발생에 직간접적으로 영향을 미치기 때문에, 시·공간적으로 연속적인 토양수분 관측이 필요하다. 과거에는 TDR (Time Domain Reflectometry) 관측 장비를 설치하여 토양수분의 변화를 관측하였으나, 이러한 지점관측의 경우 하나의 관측지점에서 토양수분을 관측하기 때문에 공간적인 토양수분 변화를 나타내지 못한다. 최근 이러한 문제를 해결하기 위하여 인공위성 이미지 자료를 이용한 토양수분 산정에 관한 연구가 활발히 수행되고 있다. 그러나 SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive)와 같은 다양한 위성에서 관측된 토양수분은 낮은 공간해상도로 인한 불확실성이 커지는 단점이 있다. 최근 이러한 한계를 극복하기 위하여 광학위성영상과 달리 날씨의 영향을 받지 않으며 고해상도 이미지자료를 제공하는 Sentinel-1A/B 위성을 활용하여 토양수분을 관측하는 연구가 진행되고 있다. Sentinel-1은 10m의 높은 공간해상도를 제공하지만, 1~2주 주기로 영상취득이 가능하기 때문에 재방문시기와 같은 시간해상도 문제가 발생한다. 따라서 본 연구에서는 Sentinel-1A/B SAR 기반 후방산란계수와 농촌진흥청에서 제공하는 TDR 기반 토양수분 실측값을 이용하여 우리나라 토양수분 공간분포를 산정하였다. 산정된 Sentinel-1A/B 기반 토양수분과 토양수분자료동화기법을 연계하여 토양의 수리학적 매개변수를 추출하였으며, 추출된 매개변수와 기상자료를 이용하여 장기간(2001~2018) 일별 토양수분 공간분포를 산정하였다.