• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.253-259
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat + ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by National Geographic Information based on aerial photograph and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The classification result by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.328-331
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• 2019

A linear frequency modulated pulse train waveform can be cost-effective in achieving high range resolution, and thus the synthetic aperture radar may be benefited by using the mixer output of the received signal. However, the image formation process from a mixer output is vulnerable to errors caused by stop-and-go approximation. In this paper, a nadir-looking imaging radar based on time domain correlation is proposed. Furthermore, to prevent the occurrence of ghosting effect in images, antenna placement on a rotating disk is proposed. Simulation results indicate that ghosting effect can be eliminated by employing the proposed antenna placement structure.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.907-917
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• 2019

Recently, the demand for high-resolution satellite images increases in many fields such as land monitoring and terrain analysis. Therefore, the need for geometric correction is increasing. As an automatic precision geometric correction method, there is a method of automatically extracting the GCP by matching between the GCP Chip and the satellite image. For automatic precision geometric correction, the success rate of matching GCP Chip and satellite image is important. Therefore, it is important to evaluate the matching performance of the manufactured GCP Chip. In order to evaluate the matching performance of GCP Chips, a total of 3,812 GCP Chips in South Korea were used as experimental data. The GCP Chip matching results of KOMPSAT-3A and Google Map showed similar matching results. Therefore, we determined that Google Map satellite imagery could replace high-resolution satellite imagery. Also, presented a method using center point and error radius of Google Map to reduce the time required to verify matching performance. As a result, it is best to set the optimum error radius to 8.5m. Evaluated the matching performance of GCP Chips in South Korea using Google Maps. And verified matching result using presented method. As a result, the GCP Chip s in South Korea had a matching success rate of about 94%. Also, the main matching failure factors were analyzed by matching failure GCP Chips. As a result, Except for GCP Chips that need to be remanufactured, the remaining GCP Chips can be used for the automatic geometric correction of satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.187-196
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• 2020

In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1139-1149
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• 2017

Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.61-69
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• 2000

Until now, the extraction of information on geographic features and the compilation of maps from satellite imagery has had many limitations because of its lower resolution compared to aerial photos to the recent. However, it is expected that the availability of high resolution satellite imagery whose spatial resolution is about 1m will reduce such limitations. Currently, a compilation of national-wide digital base maps is going on to construct the National Geographic Information Systems in Korea. It will be used for many application field of the social welfare. Therefore, in this study, we suggest that satellite imagery can help it and we have experimented on the possibility of detecting and interpreting geographic data using satellite imagery of various spatial resolutions. The interpretability and detectability of 46 features in 6 categories was experimented with 6 kinds of images of different resolutions. As a subsequent procedure, we have performed the fractal analysis for a quality test of the texture information. Through the fractal analysis, we could show that texture information and probability of discrimination increases as the spatial resolution of the image increases. Based on the results of this experiment, we could suggest the possibility of the renewal and construction of the National-wide Geographic Information Systems database using satellite imagery, as well as of examining appropriate spatial resolutions for objects of interest.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1445-1462
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• 2022

Compact Advanced Satellite 500-4 (CAS500-4), which is scheduled to be launched in 2025, is a mid-resolution satellite with a 5 m resolution developed for wide-area agriculture and forest observation. To utilize satellite images, it is important to establish a precision sensor model and establish accurate geometric information. Previous research reported that a precision sensor model could be automatically established through the process of matching ground control point (GCP) chips and satellite images. Therefore, to improve the geometric accuracy of satellite images, it is necessary to improve the GCP chip matching performance. This paper proposes an improved GCP chip matching scheme for improved precision sensor modeling of mid-resolution satellite images. When using high-resolution GCP chips for matching against mid-resolution satellite images, there are two major issues: handling the resolution difference between GCP chips and satellite images and finding the optimal quantity of GCP chips. To solve these issues, this study compared and analyzed chip matching performances according to various satellite image upsampling factors and various number of chips. RapidEye images with a resolution of 5m were used as mid-resolution satellite images. GCP chips were prepared from aerial orthographic images with a resolution of 0.25 m and satellite orthogonal images with a resolution of 0.5 m. Accuracy analysis was performed using manually extracted reference points. Experiment results show that upsampling factor of two and three significantly improved sensor model accuracy. They also show that the accuracy was maintained with reduced number of GCP chips of around 100. The results of the study confirmed the possibility of applying high-resolution GCP chips for automated precision sensor modeling of mid-resolution satellite images with improved accuracy. It is expected that the results of this study can be used to establish a precise sensor model for CAS500-4.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.83-91
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• 2012

Crown density, which is defined as the proportion of the forest floor concealed by tree crown, is important and useful information in various fields. Previous methods of measuring crown density have estimated crown density by interpreting aerial photographs or through a ground survey. These are time-consuming, labor-intensive, expensive and inconsistent approaches, as they involve a great deal of subjectivity and rely on the experience of the interpreter. In this study, the crown density of a forest in Korea was estimated using KOMPSAT-2 high-resolution satellite images. Using the image segmentation technique and stand information of the digital forest map, the forest area was divided into zones. The crown density for each segment was determined using the discriminant analysis method and the forest ratio method. The results showed that the accuracy of the discriminant analysis method was about 60%, while the accuracy of the forest ratio method was about 85%. The probability of extraction of candidate to update was verified by comparing the result with the digital forest map.