• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.589-597
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• 2019

The purpose of this study was improve accuracy the IOPs inversion model(IOPs-IM) developed in 2016 for phycocyanin(PC) concentration estimation in the Nakdong River. Additionally, two optimum models were developed and evaluated with 2017 measurement field spectral data for the Geum River and the Yeongsan River. The used measurement data for IOPs-IM analyzation was randomly classified as training and verification materials at the ratio of 2:1 in all data sets. Using the training data set from 2015-2017, accuracy results of the IOPs-IM generally improved for the Nakdong River. The RMSE(Root Mean Square Error) decreased by 14 % compared to 2016. For the GeumRiver, the results of the IOPs-IM were suitable, except for some point results in 2016. Results of the IOPs-IM in the Yeongsan River followed the overall 1:1 line and MAE(Mean Absolute Error) was lower than other rivers. But the RMSE and MAE values were higher. As a result of applying the validation data to the IOPs-IM, the accuracy of the Nakdong River was reduced to RMSE 17.7 % and MRE 16.4 %, respectively compared with 2016. However, the MRE(Mean Relative Error) was estimated to be higher by 400 % in the Geum River, and the RMSE was more than 100 mg/㎥ of the Yeongsan River. Therefore, it is necessary to get the continuously data with various sections of each river for obtain objective and reliable results and the models should be improved.

• Korean Security Journal
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• no.14
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• pp.311-336
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• 2007

This study examines the concepts of counter-act against the Bio-terrorism and the reaction system in advanced countries, thereby to find out reaction system necessary to Korea. Acts on anti-Bio-terrorism is divided to detection stage, protection stage, diagnosis stage and detoxication and neutralization stage according to flow of event occurrence. As for detection stage, Korea is developing it as contact type, while advanced countries are under development of the devices that may detect the terrorism from the remote distance. It is necessary for Korea to develop the remote-distant detection system as well as the contact type of device that may promptly operate. Among the protection gears, the quality of Korea's gas mask is recognized worldwide, but that of other outfits should be improved by applying the state-of-art science technology. The diagnosis device also should be developed to the extent that the dispatched initial action team may make immediate decisions necessary in the field. As the current trends for detoxication materials worldwide require the improvement to new materials harmless to human body and equipment, Korea is also required to acquire those materials. The technology for neutralization means the development of vaccine and antibiotics and it requires the development made by shared efforts worldwide. For this purpose, it is necessary to further develop Korea's medical technology. In addition, the further efforts are required in terms of reaction manual, training model, public communication efforts and preparation for trauma syndrome.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.471-482
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• 2016

This paper presents an extraction method for water body which uses block-based image partitioning and extension of water body boundaries to improve the performance of supervised classification for water body extraction. The Mahalanobis distance image is created by computing the spectral information of Normalized Difference Water Index (NDWI) and Near Infrared (NIR) band images over a training site within the water body in order to extract an initial water body area. To reduce the effect of noise contained in the Mahalanobis distance image, we apply mean curvature diffusion to the image, which controls diffusion coefficients based on connectivity strength between adjacent pixels and then extract the initial water body area. After partitioning the extracted water body image into the non-overlapping blocks of same size, we update the water body area using the information of water body belonging to water body boundaries. The update is performed repeatedly under the condition that the statistical distance between water body area belonging to water body boundaries and the training site is not greater than a threshold value. The accuracy assessment of the proposed algorithm was tested using KOMPSAT-2 images for the various block sizes between $11{\times}11$ and $19{\times}19$. The overall accuracy and Kappa coefficient of the algorithm varied from 99.47% to 99.53% and from 95.07% to 95.80%, respectively.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1037-1052
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• 2019

We had developed in preceding study a classification model for the Korean pine and Larch with an accuracy of 98 percent using Hyperion and Sentinel-2 satellite images, texture information, and geometric information as the first step for tree species mapping in the inaccessible North Korea. Considering a share of major tree species in North Korea, the classification model needs to be expanded as it has a large share of Oak(29.5%), Pine (12.7%), Fir (8.2%), and as well as Larch (17.5%) and Korean pine (5.8%). In order to classify 5 major tree species, national forest type map of South Korea was used to build 11,039 training and 2,330 validation data. Sentinel-2 data was used to derive spectral information, and PlanetScope data was used to generate texture information. Geometric information was built from SRTM DEM data. As a machine learning algorithm, Random forest was used. As a result, the overall accuracy of classification was 80% with 0.80 kappa statistics. Based on the training data and the classification model constructed through this study, we will extend the application to Mt. Baekdu and North and South Goseong areas to confirm the applicability of tree species classification on the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1083-1094
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• 2021

In order to monitor nuclear activity in inaccessible areas, it is necessary to establish a methodology to analyze changesin nuclear activity-related objects using high-resolution satellite images. However, traditional object detection and change detection techniques using satellite images have difficulties in applying detection results to various fields because effects of seasons and weather at the time of image acquisition. Therefore, in this paper, an object of interest was detected in a satellite image using a deep learning model, and object changes in the satellite image were analyzed based on object detection results. An initial training of the deep learning model was performed using an open dataset for object detection, and additional training dataset for the region of interest were generated and applied to transfer learning. After detecting objects by multitemporal and multisensory satellite images, we tried to detect changes in objects in the images by using them. In the experiments, it was confirmed that the object detection results of various satellite images can be directly used for change detection for nuclear activity-related monitoring in inaccessible areas.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1889-1900
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• 2022

Roads are an essential factor in the physical functioning of modern society. The spatial information of the road has much longer update cycle than the traffic situation information, and it is necessary to generate the information faster and more accurately than now. In this study, as a way to achieve that goal, the Pan-sharpening technique was applied to satellite images of Kompsat 3 and 3A to improve spatial resolution. Then, the data were used for road extraction using the semantic segmentation technique, which has been actively researched recently. The acquired Kompsat 3/3A pan-sharpened images were trained by putting it into a U-Net based segmentation model along with Massachusetts road data, and the applicability of the images were evaluated. As a result of training and verification, it was found that the model prediction performance was maintained as long as certain conditions were maintained for the input image. Therefore, it is expected that the possibility of utilizing satellite images such as Kompsat satellite will be even higher if rich training data are constructed by applying a method that minimizes the impact of surrounding environmental conditions affecting models such as shadows and surface conditions.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.42-42
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• 2022

지금까지도 유역에서의 녹조 모니터링은 현장채수를 통한 점 단위 모니터링에 크게 의존하고 있어 기후, 유속, 수온조건 등에 따라 수체에 광범위하게 발생하는 녹조를 효율적으로 모니터링하고 대응하기에는 어려운 점들이 있어왔다. 또한, 그동안 제한된 관측 데이터로 인해 현장 측정된 실측 데이터 보다는 녹조와 관련이 높은 NDVI, FGAI, SEI 등의 파생적인 지수를 산정하여 원격탐사자료와 매핑하는 방식의 분석연구 등이 선행되었다. 본 연구는 녹조의 모니터링시 정확도와 효율성을 향상을 목표로 하여, 우선은 녹조 측정장비를 활용, 7000개 이상의 녹조 관측 데이터를 확보하였으며, 이를 바탕으로 동기간의 고해상도 위성 자료와 실측자료를 매핑하기 위해 다양한Machine Learning기법을 적용함으로써 그 효과성을 검토하고자 하였다. 연구대상지는 낙동강 내성천 상류에 위치한 영주댐 유역으로서 데이터 수집단계에서는 면단위 현장(in-situ) 관측을 위해 2020년 2~9월까지 4회에 걸쳐 7291개의 녹조를 측정하고, 동일 시간 및 공간의 Sentinel-2자료 중 Band 1~12까지 총 13개(Band 8은 8과 8A로 2개)의 분광특성자료를 추출하였다. 다음으로 Machine Learning 분석기법의 적용을 위해 algae_monitoring Python library를 구축하였다. 개발된 library는 1) Training Set과 Test Set의 구분을 위한 Data 준비단계, 2) Random Forest, Gradient Boosting Regression, XGBoosting 알고리즘 중 선택하여 적용할 수 있는 모델적용단계, 3) 모델적용결과를 확인하는 Performance test단계(R², MSE, MAE, RMSE, NSE, KGE 등), 4) 모델결과의 Visualization단계, 5) 선정된 모델을 활용 위성자료를 녹조값으로 변환하는 적용단계로 구분하여 영주댐뿐만 아니라 다양한 유역에 범용적으로 적용할 수 있도록 구성하였다. 본 연구의 사례에서는 Sentinel-2위성의 12개 밴드, 기상자료(대기온도, 구름비율) 총 14개자료를 활용하여 Machine Learning기법 중 Random Forest를 적용하였을 경우에, 전반적으로 가장 높은 적합도를 나타내었으며, 적용결과 Test Set을 기준으로 NSE(Nash Sutcliffe Efficiency)가 0.96(Training Set의 경우에는 0.99) 수준의 성능을 나타내어, 광역적인 위성자료와 충분히 확보된 현장실측 자료간의 데이터 학습을 통해서 조류 모니터링 분석의 효율성이 획기적으로 증대될 수 있음을 확인하였다.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.577-587
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• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.