• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1771-1777
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• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• Spatial Information Research
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• v.20 no.5
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• pp.25-35
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• 2012

This study compared the influence of water quality according to the data sources of spatial information. Firstly, land cover map was constructed through image classification of Daecheong-dam basin and the accuracy of image classification from satellite image showed high as 88.76% in comparison with the large-scaled land cover map in Ministry of Environment, to calculate Event Mean Concentration (EMC) by land cover that impact on the evaluation of nonpoint source pollutant loads. Also curve number and direct runoff were calculated by spatial overlay with soil map and land cover map from image classification. And Seokcheon and Daecheong-Dam basin showed high in the analysis of curve number and direct runoff. Samgacheon-Joint and Sokcheon-Downstream basin showed high in the nonpoint source pollutant loads of BOD from direct runoff and EMC. And Samgacheon-Joint and Bonghwangcheon- Downstream basin showed high in the nonpoint source pollutant loads of TN and TP. Nonpoint source pollutant loads from image classification were compared with those by the land cover map from Ministry of Environment to present the effectivity of nonpoint source pollutant loads from satellite image. And Daecheong-Dam Upstream basin showed high as 10.64%, 11.70% and 20.00% respectively in the errors of nonpoint source pollutant loads of BOD, TN, and TP. Therefore, it is desirable that spatial information including with paddy and dry field is applied to the evaluation of nonpoint source pollutant loads in order to simulate water quality of basin effectively.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.242-242
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• 2015

Monitoring the land cover changes in Nakdong River Basins using the multi-temporal remote sensing datasets is necessary for preserving properties in the river basins and monitoring the environmental changes in the river basins after the 4 major river restoration project. This research aims to monitor the land cover changes using the multi-temporal Landsat imageries and the airborne topographic LiDAR data. Firstly, the river basin boundaries are determined by using the LiDAR data, and the multiple river basin imageries are generated from the multi-temporal Landsat imageries by using the river basin boundaries. Next the classification method is employed to identify the multiple land covers in the generated river basin imageries. Finally, monitoring the land cover changes is implemented by comparing the differences of the same clusters in the multi-temporal river basin imageries.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.933-940
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• 2020

Land-use change matrix data is important for calculating the LULUCF (land use, land use change and forestry) sector of the national greenhouse gas inventory. In this study, land cover changes in 2004 and 2019 were compared using the Wall-to-Wall technique with a land cover map of Sejong City from the Ministry of Environment. Sejong City was classified into six land use classes according to the Intergovernmental Panel on Climate Change (IPCC) guidelines: Forest land, crop land, grassland, wetland, settlement and other land. The coordinate system of the land cover maps of 2004 and 2019 were harmonized and the land use was reclassified. The results indicate that during the 15 years from 2004 to 2019 forestlands and croplands decreased from 50.4% (234.2 ㎢) and 34.6% (161.0 ㎢) to 43.4% (201.7 ㎢) and 20.7% (96.2 ㎢), respectively, while Settlement and Other land area increased significantly from 8.9% (41.1 ㎢) and 1.4% (6.9 ㎢) to 35.6% (119.0 ㎢) and 6.5% (30.3 ㎢). 79.㎢ of cropland area (96.2 ㎢) in 2019 was maintained as cropland, and 8.8 ㎢, 1.7 ㎢, 0.5 ㎢, 5.4 ㎢, and 0.4 ㎢ were converted from forestland, grassland, wetland, and settlement, respectively. This research, however, is subject to several limitations. The uncertainty of the land use change matrix when using the wall-to-wall technique depends on the accuracy of the utilized land cover map. Also, the land cover maps have different resolutions and different classification criteria for each production period. Despite these limitations, creating a land use change matrix using the Wall-to-Wall technique with a Land cover map has great advantages of saving time and money.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• Proceedings of the KSRS Conference
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• v.2
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• pp.555-558
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• 2006

In recent years, urban development has expanded rapidly in Nawabshah City of Pakistan. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. The core objective of this study are to provide time-series information to define and measure the urban land cover changes of Nawabshah, Pakistan between the years 1992 and 2002, and to examine related urbanization impacts on air quality of the study area. Two multi-temporal Landsat images acquired in 1992 and 2002 together with standard topographical maps to measure land cover changes were used in this study. The image processing and data manipulation were conducted using algorithms supplied with the ERDAS Imagine software. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 84 percent to 92 percent. Land cover statistics demonstrate that during the study period (1992-2002) extensive transformation of barren and vegetated lands into urban land have taken place in Nawabshah City. Results revealed that land cover changes due to urbanization has not only contaminated the air quality of the study area but also raised the health concerns for the local residents.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.2
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• pp.97-104
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• 2007

A/R CDM(Afforestation/Reforestation Clean Development Mechanism) in Kyoto Mechanism means, either afforestation in the area used for other purposes more than 50 years or reforestation in the area used for other purposes on December 31st in 1989. South Korea has few sites due to the successful forestation in the past, but North Korea has not reforested the deforested lands since the mid-1970's. So these areas need to apply A/R CDM Project for restoration. The purposes of this study are to make a time series analysis in deforested areas and to estimate a feasibility of A/R CDM. To find the site satisfying A/R CDM business definition, land cover classification was applied using satellite images of the mid-1970's with good forestation, late 1980's including A/R CDM base year, and recent 2000's, and the chronological change was analyzed to categorize the possible sites. The North Korean topographical map of 1977 was used to verify land cover classification degree of 1970's, the land cover classification results made by the Ministry of Environment in 2000 were compared to verify the accuracy of 1980's results, and the land cover classification results in 2000's were verified by 2 site visits. The results of this study can be summarized as follows. The eligible A/R CDM sites are 605,156ha on the basis of the forestation change analysis in North Korea. Since the mid-1970's, 30.8% of the decreased forestation area of 1,966,306ha was classified into A/R CDM eligible sites. While other countries have the limited eligible sites, which has not been used for forestation since 1989 or which is being scattered, North Korea has large scale sites. Deforested sites are mainly around road and residential area, consequently give better accessibility for forestation than other countries. In conclusion, it is found that North Korea can provide efficient site for applying A/R COM Project to forestation restoring deforested land because of easy accessibility and existence of many possible sites due to artificial deforestation. Also, it is meaningful that the study suggests the application possibility of A/R COM Project to restore deforested land in North Korea and the related basic information through the chronological classification of the mid-1970's with good forestation, the late-1980's including A/R COM base year, and recent 2000's. It is expected that the study contributes to revitalization of A/R CDM Project and related research on North Korea forestation.