• Journal of The Geomorphological Association of Korea
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• v.20 no.4
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• pp.85-99
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• 2013

The objective of this paper is to analyze the spatial distribution of errors in the DEM generated using waterlines from multi-temporal remote sensing data and to assess flood vulnerability. Unlike conventional research in which only global statistics of errors have been generated, this paper tries to quantitatively analyze the spatial distribution of errors from a probabilistic viewpoint using geostatistical simulation. The initial DEM in Baramarae tidal flats was generated by corrected tidal level values and waterlines extracted from multi-temporal Landsat data in 2010s. When compared with the ground measurement height data, overall the waterline-based DEM underestimated the actual heights and local variations of the errors were observed. By applying sequential Gaussian simulation based on spatial autocorrelation of DEM errors, multiple alternative error distributions were generated. After correcting errors in the initial DEM with simulated error distributions, probabilities for flood vulnerability were estimated under the sea level rise scenarios of IPCC SERS. The error analysis methodology based on geostatistical simulation could model both uncertainties of the error assessment and error propagation problems in a probabilistic framework. Therefore, it is expected that the error analysis methodology applied in this paper will be effectively used for the probabilistic assessment of errors included in various thematic maps as well as the error assessment of waterline-based DEMs in tidal flats.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.689-698
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• 2017

In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.252-262
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• 2020

In Remote Sensing, a machine learning based SVM model is typically utilized for land cover classification. And study using neural network models is also being carried out continuously. But study using high-resolution imagery of KOMPSAT is insufficient. Therefore, the purpose of this study is to assess the accuracy of land cover classification by neural network models using high-resolution KOMPSAT-3 satellite imagery. After acquiring satellite imagery of coastal areas near Gyeongju City, training data were produced. And land cover was classified with the SVM, ANN and DNN models for the three items of water, vegetation and land. Then, the accuracy of the classification results was quantitatively assessed through error matrix: the result using DNN model showed the best with 92.0% accuracy. It is necessary to supplement the training data through future multi-temporal satellite imagery, and to carry out classifications for various items.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.649-663
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• 2019

The 2018 United Nations Development Programme (UNDP) report announced that deforestation in North Korea is the most extreme situation and in terms of climate change, this deforestation is a global scale issue. To respond deforestation, various study and projects are conducted based on remote sensing, but access to public data in North Korea is limited, and objectivity is difficult to be guaranteed. In this study, the forest detection based on density estimation in statistic using Landsat imagery was conducted in Gangwon province which is the only administrative district divided into South and North. The forest spatial data of South Korea was used as data for the labeling of forest and Non-forest in the Normalized Difference Vegetation Index (NDVI), and a threshold (0.6658) for forest detection was set by Gaussian Probability Density Function (PDF) estimation by category. The results show that the forest area decreased until the 2000s in both Korea, but the area increased in 2010s. It is also confirmed that the reduction of forest area on the local scale is the same as the policy direction of urbanization and industrialization at that time. The Kappa value for validation was strong agreement (0.8) and moderate agreement (0.6), respectively. The detection based on the Gaussian PDF estimation is considered a method for complementing the statistical limitations of the existing detection method using satellite imagery. This study can be used as basic data for deforestation in North Korea and Based on the detection results, it is necessary to protect and restore forest resources.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.681-692
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• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.