• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.101-110
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• 2006

The accuracy of rural land cover using airborne multispectral images and LEAR (Light Detection And Ranging) data was analyzed. Multispectral image consists of three bands in green, red and near infrared. Intensity image was derived from the first returns of LIDAR, and vegetation height image was calculated by difference between elevation of the first returns and DEM (Digital Elevation Model) derived from the last returns of LIDAR. Using maximum likelihood classification method, three bands of multispectral images, LIDAR vegetation height image, and intensity image were employed for land cover classification. Overall accuracy of classification using all the five images was improved to 85.6% about 10% higher than that using only the three bands of multispectral images. The classification accuracy of rural land cover map using multispectral images and LIDAR images, was improved with clear difference between heights of different crops and between heights of crop and tree by LIDAR data and use of LIDAR intensity for land cover classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.77-83
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• 2008

In this study, land cover classification and NDVI evaluation for hazard mitigation precaution are carried out in surrounding areas of Yeongi-gun, Chungcheongnam-do ($132\;km^2$) where a project for multi-functional administrative city is promoted by government. Image acquired from KOMPSAT 2, LANDSAT and ASTER is utilized and comparative evaluation on limitation in classification based on resolution was carried out. The area mainly consists of arable land including mountains, rice fields, ordinary fields, etc thus special attention was paid to the classification of rice fields and ordinary fields. For the classification of image acquired from KOMPSAT 2, segmentation technique for classification of high-resolution image was applied. To evaluate the accuracy of the classification, field investigation was conducted to examine the sample and it was compared with the land usage and classification of land category in land ledger of Korea. Acquired results were made into theme map in shape file format and it would be of great help in decision making of policy for the future-oriented development plan of multi-functional administrative city.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.303-306
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• 2006

이 연구에서는 C 밴드 SAR 자료이면서 서로 다른 편광 상태의 자료를 제공할 수 있는 다중 시기 Radarsat-1 자료와 ENVISAT ASAR 자료를 이용한 토지 피복 분류를 수행하였다. 다중 시기/편광 자료로부터 평균 후방산란계수, 시간적 변이도, 긴밀도 등의 특징을 기본적으로 추출하였고, 이외에 상호 비교를 위해 주성분 분석을 이용한 특징 추출을 시도하였다. 특징들을 이용한 분류기법으로는 Random Forests를 적용하였다. 충남 예당평야 일대를 대상으로 사례연구를 수행한 결과, 주성분 분석을 통한 특징과 다편광 자료를 이용하였을 때 분류 정확도가 향상되는 것으로 나타났다.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.105-118
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• 2016

The purpose of this study is to improve the classification system of sub-divided land cover map among the land cover maps provided by the Ministry of Environment. To accomplish the purpose, first, the overseas country land cover map classification items were examined in priority. Second, the area ratio of each item established by applying the previous sub-divided classification system was analyzed. Third, the survey on the improvement of classification system targeting the users (experts and general public) who actually used the sub-divided land cover map was carried out. Fourth, a new classification system which improved the previous system by reclassifying 41 classification items into 33 items was finally established. Fifth, the established land cover classification items were applied on study area, and the land cover classification result according to the improvement method was compared with the previous classification system. Ilsan area in Goyang city where there are diverse geographic features with various land surface characteristics such as the urbanization area and agricultural land were distributed evenly were selected as the study area. The basic images used in this study were 0.25 m aerial ortho-photographs captured by the National Geographic Information Institute (NGII), and digital topographic map, detailed stock map plan, land registration map and administrative area map were used as the relevant reference data. As a result of applying the improved classification system into the study area, the area of culture-sports, leisure facilities was $1.84km^2$ which was approximately more than twice larger in comparison to the previous classification system. Other areas such as transportation and communication system and educational administration facilities were not classified. The result of this study has meaningful significance that it reflects the efficiency for the establishment and renewal of sub-divided land cover map in the future and actual users' needs.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.169-174
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• 2006

도시지역의 급변하는 토지이용의 패턴 및 토지피복상태 등의 도시환경의 변화를 분석하는 것은 도시계획 및 개발계획을 기획, 입안하는데 중요한 자료로 활용될 수 있을 것이다. 본 연구에서는 구미시를 대상지역으로 하는 Landsat TM과 Landsat $ETM^+$ 인공위성 영상데이터로부터 토지피복/토지이용 분류를 수행함으로써 18년간의 광역적 도시변화를 탐지하였다. 또한 도시의 발전과 지표면 온도의 상관성을 알아보기 위하여 열적외선 파장영역을 이용하여 온도를 추출하였다. 시가지 확장으로 인한 지표면 온도의 상승을 확인하고 이를 통해 토지이용/토지피복의 상관관계 분석 및 식생지수분포를 비교하였다.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.9-17
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• 2017

Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.