• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.59-68
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• 1999

The study for detecting suspended sediment distribution in Lake Sihwa, which has a large surface area and coastal area, using remote sensing technique was carried out with development of satellite data collected since 1970. The research, however, analysis of spatial distribution and quantity, is not common in domestic study and useful algorithms have not been proposed. In this study, a suspended sediment algorithm was composed with in-situ data obtained in study area and remote sensing reflectance obtained in-water optical instrument, which has SeaWiFS wavelength bands. However, when the algorithm was applied to Landsat TM data, including an in-situ data set, and some problems arose. The composition of the algorithm which was structured with band difference and band ratio showed the correlation of $R^2$=0.7649 with concentration of suspended sediments. And, between calculated and observed concentration of suspended sediments there was a correlation of $R^2$=0.6959. However, remote sensing reflectance obtained from Landsat TM is not good for the estimation of concentration of suspended sediments, because of high concentration of chlorophyll and CDOM(colored dissolved organic matter).

• Journal of the Korean earth science society
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• v.38 no.7
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• pp.535-545
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• 2017

Maritime accidents around the Korean Peninsula are increasing, and the ship detection research using remote sensing data is consequently becoming increasingly important. This study presented a new ship detection algorithm using hyperspectral images that provide the spectral information of several hundred channels in the ship detection field, which depends on high resolution optical imagery. We applied a spectral matching algorithm between the reflection spectrum of the ship deck obtained from two field observations and the ship and seawater spectrum of the hyperspectral sensor of an airborne visible/infrared imaging spectrometer. A total of five detection algorithms were used, namely spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM), and spectral information divergence (SID). SDS showed an error in the detection of seawater inside the ship, and SAM showed a clear classification result with a difference between ship and seawater of approximately 1.8 times. Additionally, the present study classified the vessels included in hyperspectral images by presenting the adaptive thresholds of each technique. As a result, SAM and SID showed superior ship detection abilities compared to those of other detection algorithms.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.199-215
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• 2020

A seagrass that is marine vascular plants plays an important role in the marine ecosystem, so periodic monitoring ofseagrass habitatsis being performed. Recently, the use of dronesthat can easily acquire very high-resolution imagery is increasing to efficiently monitor seagrass habitats. And deep learning based on a convolutional neural network has shown excellent performance in semantic segmentation. So, studies applied to deep learning models have been actively conducted in remote sensing. However, the segmentation accuracy was different due to the hyperparameter, various deep learning models and imagery. And the normalization of the image and the tile and batch size are also not standardized. So,seagrass habitats were segmented from drone-borne imagery using a deep learning that shows excellent performance in this study. And it compared and analyzed the results focused on normalization and tile size. For comparison of the results according to the normalization, tile and batch size, a grayscale image and grayscale imagery converted to Z-score and Min-Max normalization methods were used. And the tile size isincreased at a specific interval while the batch size is allowed the memory size to be used as much as possible. As a result, IoU was 0.26 ~ 0.4 higher than that of Z-score normalized imagery than other imagery. Also, it wasfound that the difference to 0.09 depending on the tile and batch size. The results were different according to the normalization, tile and batch. Therefore, this experiment found that these factors should have a suitable decision process.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.415-421
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• 2013

A new approach to retrieve the single-scattering albedo (SSA) of Asian dust plume, mixed with pollution particles, using multi-wavelength Raman lidar system was suggested in this study. Asian dust plume was separated as dust and non-dust particle (i.e. spherical particle) by the particle depolarization ratio at 532 nm. The vertical profiles of optical properties (the particle extinction coefficient at 355 and 532 nm and backscatter coefficient at 355, 532 and 1064 nm) for non-dust particle were used as input parameter for the inversion algorithm. The inversion algorithm provides the vertical distribution of microphysical properties of non-dust particle only so that the estimation of the SSA for the Asian dust in mixing state was suggested in this study. In order to estimate the SSA for the mixed Asian dust, we combined the SSA of non-dust particles retrieved by the inversion algorithms with assumed the SSA of 0.96 at 532 nm for dust. The retrieved SSA of Asian dust plume by lidar data was compared with the Aerosol Robotics Network (AERONET) retrieved values and showed good agreement.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.493-502
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• 2021

Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.383-383
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• 2015

하천의 하상고 측량은 하상변동 분석, 서식처 구조 등을 이해하는데 매우 중요한 정보를 제공한다. 하지만, 현재까지 대부분의 하상고 측량은 일정한 간격의 하천 단면 측량에 의해서 행해져 왔다. 최근 GPS와 다중 빔 측심기를 이용하여 하상의 3차원적 형상을 조밀하게 측량하고 있으나 비용이 많이 들기 때문에 긴 하천 구간을 전부 측량하지는 못하고 특정한 부분에 대해서만 집중하고 있다. 항공 LiDAR의 경우 넓은 지역에 대해 신속하고 고해상도로 지형을 측량할 수 있으나 수중 투과 장비가 고가이며, 일반 적색 레이져 기반 LiDAR는 수중을 측정하지 못하여 하상 측량에 한계가 있다. 이에 대한 대안으로 활용할 수 있는 방법은 광학 기반의 원격 탐사에 의한 수심 측량 방법이다. 이 방법은 얕은 수심의 하천에 대한 활용되었는데, 광학 센서 이미지나 항공사진 등을 이용한다. 본 연구에서는 저고도에서 촬영한 고해상도 디지털 항공사진을 이용하여 모래하천의 수심을 추정하였다. 이 방법은 항공사진의 적색 및 녹색 색상값과 현장에서 정밀한 측위 하에 측량한 수심값 사이의 관계를 이용한다. 이를 통해 보정식을 수립하고 검사 자료를 이용하여 검증한 후 항공사진의 해당 지역에 대해 수심 부분을 마스킹 처리하여 하상고를 구축하였다. 검사 자료에 대한 RMSE는 약 12 cm로 나타났다. 이를 활용하여 대상 구간의 3차원적 지형 형상을 구축하였다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.291-292
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• 2009

항공레이저측량(LiDAR)는 대상물에 반사되어 오는 레이저를 이용하여 멀리 위치한 대상물의 정보를 측정하는 광학원격탐사기술이다. 항공기에 레이저센서를 장착, 지표면에 레이저 펄스를 주사하여 반사된 펄스의 도달시간을 측정, 반사지점의 3차원 위치좌표를 계산하여 지표면의 정보를 추출한다. 항공레이저측량(LiDAR)는 재난재해관리, 도로 및 교통, 시설물계획관리, 지역지구 계획 등에 활용할 수 있다. 또한 항공사진과 GIS를 활용함으로서 실세계의 3차원 표현이 가능하며 실세계를 인터넷, PDA 및 핸드폰 등에 3차원으로 표현하는데 필요한 데이터베이스를 제공할 수 있다. 이는 도래하는 유비쿼터스 시대에 필요한 정확하고 정교한 데이터기반을 구성한다.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.109-110
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• 2013

도심화는 녹지의 제거로 인해 야기괴는 다양한 환경문제를 일으킨다. 이러한 도심화 현상은 종종 원격탐사영상을 이용하여 평가되는데, 대표적으로 정규식생지수와 지표온도를 통해 이루어진다. 그러나 식생지수는 두 개의 광학채널을 이용하기 때문에 식생이 아닌 도심지에서 식생과 같은 지수를 나타나는 오류가 발생한다. 이에 본 연구에서는 도심지에서 정규식생지수와 지표온도가 도심지 표면의 물질의 혼합에 따라 어떻게 변화하는지 관찰하였다.