• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.359-364
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• 2022

The change detection in remote sensing results in errors due to the camera's optical factors, seasonal factors, and land cover characteristics. The inclination of the building in the image was simulated according to the camera angle using the Cycle Generative Adversarial Network method, and the simulated image was used to contribute to the improvement of change detection accuracy. Based on CycleGAN, the inclination of the building was similarly simulated to the building in the other image based on the image of one of the two periods, and the error of the original image and the inclination of the building was compared and analyzed. The experimental data were taken at different times at different angles, and Kompsat-3A high-resolution satellite images including urban areas with dense buildings were used. As a result of the experiment, the number of incorrect detection pixels per building in the two images for the building area in the image was shown to be reduced by approximately 7 times from 12,632 in the original image and 1,730 in the CycleGAN-based simulation image. Therefore, it was confirmed that the proposed method can reduce detection errors due to the inclination of the building.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.449-459
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• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.516-516
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• 2002

With the wide usage of LiDAR data and high-resolution satellite image, 3D modeling of buildings in urban areas has become an important research topic in the photogrammetry and computer vision field for many years. However the previous modeling has its limitations of merely texturing the image to the DSM surface of the study area and does not represent the relief of building surfaces. This study is focused on presenting a system of realistic 3D building modeling from consecutive stereo image sequences using digital camera. Generally when acquiring images through camera, various parameters such as zooming, focus, and attitude are necessary to extract accurate results, which in certain cases, some parameters have to be rectified. It is, however, not always possible or practical to precisely estimate or rectify the information of camera positions or attitudes. In this research, we constructed the collinearity condition of stereo images through extracting the distinctive points from stereo image sequence. In addition, we executed image matching with Graph Cut method, which has a very high accuracy. This system successfully performed the realistic modeling of building with a good visual quality. From the study, we concluded that 3D building modeling of city area could be acquired more realistically.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.417-425
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• 2006

This Paper Proposes a method of building a panoramic video from several videos captured from adjacent cameras. The panoramic image which constructed from adjacent and overlapped images is used for photogrammetry, satellite photo or many computer graphic applications. The perspective transformation, which is estimated from the appropriate corresponding pairs of images, can be used to construct the panoramic image without unwarranted distortion and its quality is decided by how to find the features needed for transform estimation. We used the corner points for the corresponding features, and morphological structures were utilized for fast and robust corner detection. We used the criterion of the corner strength, which guarantees the robust detection of the corner in most situations. For the transformation, 8 parameters were estimated from perspective equations which use matched points of adjacent images, and bilinear color blending was used to construct a soapless panoramic video. The experiments showed that the proposed method yields fast results with good quality under various conditions.

• Korean Journal of Geomatics
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• v.1 no.1
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• pp.1-5
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• 2001

This paper presents an investigation into the operational comparison of SPOT triangulation to build GCP library by analytical plotter and DPW (digital photogrammetric workstation). GCP database derived from current SPOT images can be used to other image sensors of satellite, if any reasons, such as lack of topographic maps or GCPs. But, general formulation of a photogrammetric process for GCP measurement has to take care of the scene interpretation problem. There are two classical methods depending on whether an analytical plotter or DPW is being used. Regardless of the method used, the measurement of GCPs is the weakest point in the automation of photogrammetric orientation procedures. To make an operational comparison, five models of SPOT panchromatic images (level 1A) and negative films (level 1AP) were used. Ten images and film products were used for the five GRS areas. Photogrammetric measurements were carried out in a manual mode on P2 analytical plotter and LH Systems DPW770. We presented an approach for exterior orientation of SPOT images, which was based on the use of approximately eighty national geodetic control points as GCPs which located on the summit of the mountain. Using sixteen well-spaced geodetic control points per model, all segments consistently showed RMS error just below the pixel at the check points in analytical instrument. In the case of DPW, half of the ground controls could not found or distinguished exactly when we displayed the image on the computer monitor. Experiment results showed that the RMS errors with DPW test was fluctuated case by case. And the magnitudes of the errors were reached more than three pixels due to the lack of image interpretation capability. It showed that the geodetic control points is not suitable as the ground control points in DPW for modeling the SPOT image.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.775-784
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• 2023

Drone images are being used more and more actively in the fields of surveying and spatial information, and are rapidly replacing existing aerial and satellite images. The technology of quickly acquiring real-time data at low cost and processing it is now being applied to actual industries beyond research. However, there are also problems encountered as this progresses. When high-resolution spatial information is acquired using a general 2D flight plan for a terrain with sever undulations, problems arise due to the difference in resolution of the data. In particular, when a low-altitude high-resolution image is taken using a drone in a mountainous or steep terrain, there may be a problem in image matching due to a resolution difference caused by terrain undulations. This problem occurs because a drone acquires data while flying on a 2D plane at a fixed altitude, just like conventional aerial photography. In order to acquire high-quality 3D data using a drone, the scale difference for the shooting distance should be considered. In addition, in order to obtain facade images of large structures, it is necessary to take images in 3D space. In this study, in order to improve the disadvantages of the 2D flight method, a 3D flight plan was established for the study area, and it was confirmed that high-quality 3D spatial information could be obtained in this way.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.1
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• pp.43-52
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• 2016

Due to the characteristics of microwave signals, Radar satellite image has been used for flood detection without weather and time influence. The more methods of flood detection were developed, the more detection rate of flood area has been increased. Since flood causes a lot of damages, flooded area should be distinguished from non flooded area. Also, the detection of flood area should be accurate. Therefore, not only image resolution but also the filtering process is critical to minimize resolution degradation. Although a resolution of radar images become better as technology develops, there were a limited focused on a highly suitable filtering methods for flood detection. Thus, the purpose of this study is to find out the most appropriate filtering method for flood detection by comparing three filtering methods: Lee filter, Frost filter and NL-means filter. Therefore, to compare the filters to detect floods, each filters are applied to the radar image. Comparison was drawn among filtered images. Then, the flood map, results of filtered images are compared in that order. As a result, Frost and NL-means filter are more effective in removing the speckle noise compared to Lee filter. In case of Frost filter, resolution degradation occurred severly during removal of the noise. In case of NL-means filter, shadow effect which could be one of the main reasons that causes false detection were not eliminated comparing to other filters. Nevertheless, result of NL-means filter shows the best detection rate because the number of shadow pixels is relatively low in entire image. Kappa coefficient is scored 0.81 for NL-means filtered image and 0.55, 0.64 and 0.74 follows for non filtered image, Lee filtered image and Frost filtered image respectively. Also, in the process of NL-means filter, speckle noise could be removed without resolution degradation. Accordingly, flooded area could be distinguished effectively from other area in NL-means filtered image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.311-321
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• 2009

The Nation Geodetic Reference System which presents a consistent location standard used in creating a map or developing national land is defined and managed by the law in a nation. Each nation had used its own geodetic system created by astronomical surveying until recently, when Geodetic Reference System(World Geodetic Reference System) has been developed and used to progress in space and satellite geodetic technologies. Korea also amended its geodetic law in December 2001, converting its national geodetic system whose reference an oval figure is Bessel ellipsoid into the World Geodetic Reference System which uses GRS80 ellipsoid as reference ellipsoid. Accordingly, the National Geography Information Institute improved law and systems related to the change for the effective conversion from its national geodetic system into the World Geodetic Reference System. In addition National geographic information institute of the results of various studies is drawn to the World Geodetic Reference System for switching technology-met some of the institutional foundation Despite of accordance with formalities, National geographic information institute, Ministry of Land, Transport and Maritime Affairs and some local government of the World Geodetic Reference System, and local government has or has not spread in public institutions. Therefore, in order to promote the switch to the World Geodetic Reference System, it is required to analyze current technical and institutional problems and obstacles of the switch to the World Geodetic Reference System and to present the resolutions and to establish policy to achieve them. Accordingly, for the promotion of the switch to the World Geodetic Reference System, this study analyzed the results of previous studies, the current state of the switch to the World Geodetic Reference System and the problems of the switch, and then offered technological and institutional supplements. Furthermore, it standardized the subject and type of the conversion, defined the scope of the tasks of the National Geographic Information Institute and its related organizations, and presented the policy direction for the overall use of the World Geodetic Reference System by 2010.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.