• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.73-81
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• 2010

In recent years, there have been diverse researches and utilizations of creating geo-spatial information with high resolution satellite images. However thematic maps made with middle or low resolution satellite images have low location accuracy and precision of thematic information. This study set out to propose a method of making a precision thematic map with high resolution satellite images by examining the conversion from the conventional method based on middle or low resolution satellite images to the automatic method based on high resolution satellite images of GSD 1m or lower, extracting thematic information of middle or large scale of 1/5,000 or lower, and analyzing its accuracy. Seven classification classes were categorized according to the object-oriented classification in order to automatically extract thematic information with high resolution satellite images. And the classification results were compared and analyzed with the old middle scale land cover map and 1/1000 digital map.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.1-12
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• 2012

The agricultural area was classified into dry and paddy fields in this study using the near-infrared band of Landsat TM to extract land cover classes that need to the application of Expected Mean Concentration (EMC) in nonpoint source works. The accuracy of image classification of the land cover map from Landsat TM image showed 83.61% and 78.41% respectively by comparing with the large and middle scale land cover map of Ministry of Environment. As the result of Soil Conservation Service (SCS) Curve Number (CN) using the land cover map from image classification, Dongbok dam and Dongbok stream basin were analyzed high. Also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of EMC of BOD, TN, TP by basin. And also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of non-point source through coupling with direct runoff. Therefore these basins were selected with the main area for the management of nonpoint source.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• Journal of Environmental Science International
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• v.17 no.6
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• pp.679-687
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• 2008

In this study, we implemented landslide distribution of Jeju Island using ANN and GIS, respectively. To do this, we first get the counter line from 1:2,5000 digital map and use this counter line to make the DEM. for the evaluate the land slide susceptibility. Next, we abstracted slop map and aspect map from the DEM and get the land use map using ISODATA classification method from Landsat 7 images. In the computation processes of landslide analysis, we make the class to the soil map, tree diameter map, Isohyet map, geological map and so on. Finally, we applied the ANN method to the landslide one and calculated its weighted values. GIS results can be calculated by using Acrview program and produced Jeju landslide susceptibility map by usign Weighted Overlay method. Based on our results, we found the relatively weak points of landslide ware concentrated to the top of Halla mountains.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2066-2070
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• 2008

본 연구는 인공신경망(Artificial Neural Networks: ANNs)기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론을 이용한다. 자기조직화 특성을 이용하여 스스로 학습이 가능하고, 구조상 수행이 빨라 학습 단계에 소요되는 시간을 줄 일 수 있는 장점을 가진 자기조직화 이론을 도입하고, 수질자료 중 전체 유기물의 양을 나타내며 난분해성 물질에 대한 해석이 가능하고 재현성이 탁월한 TOC 와 강우-유출량 자료의 분포적 양상과 특징을 분석하여 예측을 위한 모형화 과정에 기여하고자 한다. 최적의 Map Size와 Map Array 결정을 위해 수집된 강우와 유출량자료 및 TOC 자료에 대해 Garcia의 경험식을 이용하여 Map을 구성하는 단위구조의 총 수(M)를 산정하여 M값에 따른 종방향 및 횡방향 크기를 결정하는 다수의 Map 크기를 검토하고, 또한 Map 배열은 2차원 배열의 사각형배열(Rectangular array)과 육각형배열(Hexagonal array)에 대해서도 복합적으로 검토하여 최적의 특성조건을 결정하여 강우-유출 및 TOC 관계의 분할특성을 분석한다.

• Journal of Environmental Impact Assessment
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• v.7 no.2
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• pp.27-35
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• 1998

In Korea, the initial installation of GIS requires lots of cost, time, and human efforts. If the accuracy of GIS data does not meet the certain standard for use, the system may not work as expected. So, it needs to be investigated for the accuracy of raw data. However, there is little study for the accuracy of raw data in Korea. Therefore, the purpose of this study is to review the data accuracy of raw data - geologic map, 1:5,000 and 1:25,000 scale topographic map, forest stand map, degree of green naturality(DGN) map, and detailed survey data of DGN map-, which are to be used in Environmental Information System(EIS) in Korea. After this study, some errors in data were surveyed and following conclusions were derived. (1) There is no map data, e. g, wildlife habitat map. (2) Some data are misinterpreted depending on the location in the geologic map. (3) Some data are not updated properly after change of topography in the topographic map or the elevation and location is different depending on the scale.. (4) Some data are not edited properly in the forest stand map, e. g. two attributes in one polygon. (5) DGN classification system does not reflect the characteristic of Korean vegetation community. So, it needs to be refined and restructured.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Journal of the Korean Institute of Rural Architecture
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• v.26 no.3
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• pp.1-9
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• 2024

This study aims to estimate the cover classification and carbon respiration of halophytes based on the issues of utilising blue carbon in recent context of climate change. To address the aims, the study classified halophytes(Triglochin maritimum L and Phragmites australis), Intertidal(non-vegetated tidal flats) and Supratidal(sandy tidal flats) to measure carbon respiration and classify cover. The results are revealed that first, the carbon respiration in vegetated areas was less than that in non-vegetated areas. Second, the cover classification could be divided into halophyte communities(Triglochin maritimum L, Phragmites australis), Intertidal and Supratidal by NDWI(Moisture Index, Normalized Difference Water Index) Third, the total carbon respiration of blue carbon was calculated to be -0.0121 Ton km² hr^-1 with halophyte communities at -0.0011 Ton km² hr^-1, Intertidal respiration at -0.0113 Ton km² hr^-1 and Supratidal respiration at 0.0003 Ton km² hr^-1. As this challenge is a fundamental study that calculates the quantitative net carbon storage based on the blue carbon-based marine ecosystem, contributing to firstly, measuring the carbon respiration of cordgrass communities, reed communities, and non-vegetated tidal flats, which are potential blue carbon candidates in the study area, to establish representative values for carbon respiration, secondly, verifying the reliability of cover classification of native halophytes extracted through image classification technology, and thirdly, challenging to create a thematic map of carbon respiration, calculating the area and carbon respiration for each classification category.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.277-292
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• 2020

The purpose of this study is to propose the possibility of precise surface sedimentary facies classification and a more accurate classification method by generating the large-scale map of surface sedimentary facies based on UAV data and object-based image analysis (OBIA) for Hwang-do tidal flat in Cheonsu bay. The very high resolution UAV data extracted factors that affect the classification of surface sedimentary facies, such as RGB ortho imagery, Digital elevation model (DEM), and tidal channel density, and analyzed the principal components of surface sedimentary facies through statistical analysis methods. Based on principal components, input data to be used for classification of surface sedimentary facies were divided into three cases such as (1) visible band spectrum, (2) topographical elevation and tidal channel density, (3) visible band spectrum and topographical elevation, tidal channel density. The object-based image analysis classification method was applied to map the classification of surface sedimentary facies according to conditions of input data. The surface sedimentary facies could be classified into a total of six sedimentary facies following the folk classification criteria. In addition, the use of visible band spectrum, topographical elevation, and tidal channel density enabled the most effective classification of surface sedimentary facies with a total accuracy of 63.04% and the Kappa coefficient of 0.54.