• Spatial Information Research
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• v.10 no.1
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• pp.139-152
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• 2002

The purpose of this study is to estimate temporal soil loss change according to long-term land cover changes using G1S and RS. Revised USLE(Universal Soil Loss Equation) factors were prepared by using point rainfall data, DEM(Digital Elevation Model), soil map and land cover map. During the past two decades, land cover changes were traced by using Landsat MSS and TM data. As a result, forest area in 2000 has decreased 25.3 $km^2$ compared with that in 1990. Soil loss has decreased 3751.2 tou/yr. On the other hand, upland area has increased 22.5 $km^2$. Soil loss of upland has increased 5395.4 to/yr. Therefore, soil loss in 2000 increased 6.3 kg/$m^2$/yr compared with that in 1990. This was mainly caused by the increased upland area.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.1
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• pp.11-18
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• 2013

Eulsukdo located in the Nakdong Estuary plays important role in ecosystem and coastal wetland. There have been various changes in Eulsukdo up to now. Recently, we expect a great change of the western part of shoreline in Eulsukdo due to the floodgate construction but there is few databases. In this study, shorelines were digitized after we had produced the ortho-images by using aerial photos taken for 30 years(8 times). SCE, NSM and EPR were analysed by DSAS 4.2 program using vector data. In addition, the changes of shoreline were analysed in October 2011 from before Eulsukdo water gate construction to now by adding field surveying with VRS. The amount of years shoreline change is -0.34m/yr in 2009(before water gate construction) and -0.50m/yr in 2011(during the water gate construction), and the change trend shows an accumulation aspect.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.43-52
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• 2004

This study presents the application of aerial photographs and the Korea Multi-Purpose Satellite, KOMPSAT-1 Electro-Optical Camera(EOC) imagery in detecting change in an urban area that has been rapidly growing. For the study, we used multi-temporal images which were acquired by two different sensors. Image registration and resampling were rallied out before performing change detection in a common reference system with the same spatial resolution. for all of the images. Results from image differencing and image ratioing techniques show that panchromatic aerial photographs and KOMPSAT-1 EOC images collected by different sensors have potential to detect changes of urban features such as building, road and other man-made structure. And the optimal threshold values were suggested in applying image differencing and image ratioing techniques for change detection.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.87-98
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• 2010

For generating accurate land cover map over the whole Korean Peninsula, post-mosaic classification method is desirable in large area where multiple image data sets are used. We try to derive an optimal mosaic method of multi-temporal Landsat ETM+ scenes for the land cover classification over the Korea Peninsula. Total 65 Landsat ETM+ scenes were acquired, which were taken in 2000 and 2001. To reduce radiometric difference between adjacent Landsat ETM+ scenes, we apply three relative radiometric correction methods (histogram matching, 1st-regression method referenced center image, and 1st-regression method at each Landsat ETM+ path). After the relative correction, we generated three mosaic images for three seasons of leaf-off, transplanting, leaf-on season. For comparison, three mosaic images were compared by the mean absolute difference and computer classification accuracy. The results show that the mosaic image using 1st-regression method at each path show the best correction results and highest classification accuracy. Additionally, the mosaic image acquired during leaf-on season show the higher radiance variance between adjacent images than other season.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.209-219
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• 2019

Satellite imagery occurs geometric and radiometric errors due to external environmental factors at the acquired time, which in turn causes false-alarm in change detection. These errors should be eliminated by geometric and radiometric corrections. In this study, we propose a methodology that automatically and simultaneously performs geometric and radiometric corrections by using the SURF (Speeded-Up Robust Feature) algorithm and the mask filter. The MPs (Matching Points), which show invariant properties between multi-temporal imagery, extracted through the SURF algorithm are used for automatic geometric correction. Using the properties of the extracted MPs, PIFs (Pseudo Invariant Features) used for relative radiometric correction are selected. Subsequently, secondary PIFs are extracted by generated mask filters around the selected PIFs. After performing automatic using the extracted MPs, we could confirm that geometric and radiometric errors are eliminated as the result of performing the relative radiometric correction using PIFs in geo-rectified images.

• 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.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.1-9
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• 2005

This study was performed to analyze time series landuse pattern of urban areas and the change of the areas by using remotely sensed multiple sensors. The results were as follows. First, according to the result of time series analysis, most agricultural land has been changed into built-up areas by development work such as the land development or land readjustment project, arrangement of science parks or military facilities, and location of public establishment like government buildings. Second, if the expansion of built-up areas maintains the present scale and speed, it seems that a lot of parts of land would be changed into built-up areas, especially centering around agricultural land, so it is necessary to establish the plan for urban space. Third, I have synthetically collected the data of the project of urban development and systematically monitored the process of in expansion the built-up areas up to now (from the past). I hereby could lay the foundation that makes us scientifically forecast the direction of expansion in the built-up areas by the urban development in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.97-104
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• 2016

In this study, analyze and monitor the change of the ecological restoration area inside the open-pit mine in Gangwon-do. and to analyze and monitor the change of ecological restoration area. analyzed the distribution of vegetation using high-resolution orthophoto of various periods and analyzed terrain change using DSM/DEM in study area. Therefore, orthophoto and 포인트 클라우드 were collected from 2014 aerial laser surveying and 2015 fixed-wing drone photogrammetry. In addition, orhtophoto and 포인트 클라우드 were produced by using rotary-wing drone photogrammety in 2016, and change of ecological restoration area was analyzed using this. As a result, it's possible to perform change monitoring of the open-pit mine ecological restoration area. using nEGI and VARI, about 10-30% of the area ratio of the result of extracting vegetation distribution area is distributed, and the comparison DSM and DEM cross section and restoration plan line, the cross section made by using the drone were similar, and the earth-volume analysis was possible.