• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.191-202
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• 2012

A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.141-148
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• 2004

The ground positions and elevations information called DEMs(Digital Elevation Models) which are extracted from the stereo aerial photographs and/or satellite images using image matching method have the natural errors caused by variant environments. This study suggests the method to evaluate DEMs using correlation values between the reference and the target DEMs. This would be strongly helpful for experts to correct these errors. To evaluate the whole area of DEMs in the horizontal and vertical errors, the target cell is matched for each reference cell using the correlation values of these two cells. When the target cell is matched for each reference cell, horizontal and vertical error was calculated so as to help experts to recognize a certain area of DEMs which should be corrected and edited. If the correlation value is low and tile difference in height is high, the target cell will be candidated as changed or corrupted cell. When the area is clustered with those candidated cells, that area will be regarded as changed or corrupted area to be corrected and edited. Using this method, the evaluation for all DEM cells is practicable, the horizontal errors as well as vertical errors is calculable and the changed or corrupted area can be detected more efficiently.

• Journal of the Korean Geographical Society
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• v.41 no.3 s.114
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• pp.331-345
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• 2006

This study deals with geomorphological process of the sand dune landform including the distribution and surface environments, characteristics of sediments, origins and moving processes in lower reach and mouth delta of Duman River, Northeast Korea and China. The methodology of the study includes image analysis of Landsat TM(1992.10) and ETM(2000.9) and Spot(2005.4) for analysis of land cover, 2 times field survey for recognition of landform and acquisition of sediments raw data materials, and grain analysis and exoscopy about raw data materials. The geomorphic elements from satellite image analysis are composed of the delta, sand spit, active and stable dune, sand bar and riparian vegetated zone. Results of the grain analysis indicate the sediments originated from marine coastal zone than riverine one. This means that present sand dune not so much reflect present climatic and geomorphic environments. Result of the exoscopy analysis show that ratio of quartz, which is comparatively resistant to environment, is highest as $65{\sim}83%$ out of sediments. But the surface of the $30{\sim}40%$ of mineral grains was coated by yellow-colored stained materials, due to chemical weathering. Some grains show rough skin, looking as acicular, network structure and etching pits, affected by physical and chemical weathering.

• Korean Journal of Remote Sensing
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• v.18 no.4
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• pp.221-231
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• 2002

In this study we applied a DEM generation algorithm developed in-house to satellite images at various resolution and discussed the results. We tested SPOT images at l0m resolution, EOC images at 6.6m and IKONOS images at 1m resolution. These images include the same urban area in Daejeon city. For camera model, we used Gupta & Hartley's(1997) DLT model for all three image sets. We carried out accuracy assessment using USGS DTED for SPOT and EOC and 23 check points for IKONOS. The assessment showed that SPOT DEM had about 38m RMS error, EOC DEM 12m RMS error and IKONOS DEM 6.5m RMS error. In terms of image resolution, SPOT and EOC DEM error corresponds to 2∼4 pixels where as IKONOS DEM error 6∼7pixels. IKONOS DEM contains more errors in pixels. However, in IKONOS DEM, individual buildings, apartments and major roads are identifiable. All three DEMs contained errors due to height discontinuity, occlusion and shadow. These experiments show that our algorithm can generate urban DEM from 1m resolution and that, however, we need to improve the algorithm to minimize effects of occlusion and building shadows on DEMs.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.21-30
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• 2009

The purposes of this paper are to present a selection method of neighboring pixels whose local statistics are similar to the center pixel and combine the selection result with mean curvature diffusion filter to reduce noises in remote sensed imagery. The order of selection of neighboring pixels is critical, especially for finding a pixel belonging to the homogeneous region, since the statistics of the homogeneous region vary according to the selection order. An effective strategy for selecting neighboring pixels, which uses rank-order differences vector obtained by computing the intensity differences between the center pixel and neighboring pixels and arranging them in ascending order, is proposed in this paper. By using region growing method, we divide the elements of the rank-ordered differences vector into two groups, homogeneous rank-ordered differences vector and outlier rank-ordered differences vector. The mean curvature diffusion filter is combined with a line process, which chooses selectively diffusion coefficient of the neighboring pixels belonging into homogeneous rank-ordered differences vector. Experimental results using an aerial image and a TerraSAR-X satellite image showed that the proposed method reduced more efficiently noises than some conventional adaptive filters using all neighboring pixels in updating the center pixel.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.637-647
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• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.158-174
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• 2018

The leaf area index (LAI) is a major factor explaining the photosynthesis of vegetation, evapotranspiration, and energy exchange between the earth surface and atmosphere, and there have been studies on accurate and applicable LAI estimation methods. This study aimed to investigate the relationship between the actual LAI data, UAV image-based vegetation index, canopy height and satellite image (Sentinel-2) LAI and to present an effective LAI estimation method using UAV. As a result, among the six vegetation indices in this study, NDRE ($R^2=0.496$) and CIRE ($R^2=0.443$), which contained red-edge band, showed a high correlation. The application of the canopy height model data to the vegetation index improved the explanatory power of the LAI. In addition, in the case of NDVI, the saturation problem caused by the linear relationship with LAI was addressed. In this study, it was possible to estimate high resolution LAI using UAV images. It is expected that the applicability of such data will be improved if calibration and correction steps are carried out for various vegetation and seasonal images.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.361-370
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• 2020

Long-term observation of the life cycle of plants allows the identification of critical signals of the effects of climate change on plants. Indeed, plant phenology is the simplest approach to detect climate change. Observation of seasonal changes in plants using digital repeat imaging helps in overcoming the limitations of both traditional methods and satellite remote sensing. In this study, we demonstrate the utility of camera-based repeat digital imaging in this context. We observed the biological events of plants and quantified their phenophases in the northern temperate type deciduous broadleaf forest of Jeombong Mountain. This study aimed to identify trends in seasonal characteristics of Quercus mongolica (deciduous broadleaf forest) and Pinus densiflora (evergreen coniferous forest). The vegetation index, green chromatic coordinate (GCC), was calculated from the RGB channel image data. The magnitude of the GCC amplitude was smaller in the evergreen coniferous forest than in the deciduous forest. The slope of the GCC (increased in spring and decreased in autumn) was moderate in the evergreen coniferous forest compared with that in the deciduous forest. In the pine forest, the beginning of growth occurred earlier than that in the red oak forest, whereas the end of growth was later. Verification of the accuracy of the phenophases showed high accuracy with root-mean-square error (RMSE) values of 0.008 (region of interest [ROI]1) and 0.006 (ROI3). These results reflect the tendency of the GCC trajectory in a northern temperate type deciduous broadleaf forest. Based on the results, we propose that repeat imaging using digital cameras will be useful for the observation of phenophases.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.475-491
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• 2021

With the recent increase in available high-resolution (< ~1 m) satellite SAR images, the demand for precise registration of SAR images is increasing in various fields including change detection. The registration between high-resolution SAR images acquired in different look angle is difficult due to speckle noise and geometric distortion caused by the characteristics of SAR images. In this study, registration is performed in two stages, coarse and fine, using the x-band SAR data imaged at staring spotlight mode of TerraSAR-X. For the coarse registration, a method combining the adaptive sampling method and SAR-SIFT (Scale Invariant Feature Transform) is applied, and three rigid methods (NCC: Normalized Cross Correlation, Phase Congruency-NCC, MI: Mutual Information) and one non-rigid (Gefolki: Geoscience extended Flow Optical Flow Lucas-Kanade Iterative), for the fine registration stage, was performed for performance comparison. The results were compared by using RMSE (Root Mean Square Error) and FSIM (Feature Similarity) index, and all rigid models showed poor results in all image combinations. It is confirmed that the rigid models have a large registration error in the rugged terrain area. As a result of applying the Gefolki algorithm, it was confirmed that the RMSE of Gefolki showed the best result as a 1~3 pixels, and the FSIM index also obtained a higher value than 0.02~0.03 compared to other rigid methods. It was confirmed that the mis-registration due to terrain effect could be sufficiently reduced by the Gefolki algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.