• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.104-113
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• 2012

Followed by that HTML5(Hypertext Markup Language5) was introduced, many kinds of program and services based on this have been developed and released. HTML5 is technical standard specifications for cross platform for personal computers and mobile devices so that it is expected that continuing progress and wide application in the both sides of the academic and the industrial fields increase. This study is to design and implement a mobile application program for overlay rendering with DEM and other geo-based image sets using HTML5 WebGL for 3D graphic processing on web environment. Particularly, the blending technique was used for overlay processing with multiple images. Among available WebGL frameworks, CubicVR.js was adopted, and various blending techniques were provided in the user interface for general users. For the actual application in the study area around the Sejong city, serveral types of geo-based data sets were used and processed: KOMPSAT-2 images, ALOS PALSAR SAR images, and grid data by environment measurements. While, DEM for 3D viewing with these geo-based images was produced using contour information of the digital map sets. This work demonstrates possibilities that new types of contents and service system using geo-based images can be extracted and applied.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.421-432
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• 2015

There are significant differences in geometric property and stereo model accuracy between single-sensor stereo that uses two images taken by stereo acquisition mechanism within identical sensor and dual-sensor stereo that randomly combines two images taken from two different sensors. This paper compares the two types of stereo pairs thoroughly. For experiment, two single-sensor stereo pairs and four dual-sensor stereo pairs were constituted using SPOT-5 stereo and KOMPSAT-2 stereo covering same area. While the two single-sensor stereos have stable geometry, the dual-sensor stereos produced two stable and two unstable geometries. In particular, the unstable geometry led to a decrease in stereo model accuracy of the dual-sensor stereos. The two types of stereo pairs were also compared under the stable geometry. Overall, single-sensor stereos performed better than dual-sensor stereos for vertical mapping, but dual-sensor stereos was more accurate for horizontal mapping. This paper has revealed the differences of two types of stereos with their geometric properties and positioning accuracies, suggesting important considerations for handling satellite stereo images, particularly for dual-satellite stereo images.

• Journal of Astronomy and Space Sciences
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• v.26 no.2
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• pp.229-236
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• 2009

KOorea Multi-purpose SATellite(KOMPSAT)-5 will be launched at 550km altitude in 2010. Accurate satellite position(20 cm) and velocity(0.03 cm/s) are required to treat highly precise Synthetic Aperture Radar(SAR) image processing. Ionosphere delay was eliminated using dual frequency GPS data and double differenced GPS measurement removed common clock errors of both GPS satellites and receiver. SAC-C carrier phase data with 0.1 Hz sampling rate was used to achieve precise orbit determination(POD) with ETRI GNSS Precise Orbit Determination(EGPOD) software, which was developed by ETRI. Dynamic model approach was used and satellite's position, velocity, and the coefficients of solar radiation pressure and drag were adjusted once per arc using Batch Least Square Estimator(BLSE) filter. Empirical accelerations for sinusoidal radial, along-track, and cross track terms were also estimated once per revolution for unmodeled dynamics. Additionally piece-wise constant acceleration for cross-track direction was estimated once per arc. The performance of POD was validated by comparing with JPL's Precise Orbit Ephemeris(POE).

• Korean Journal of Remote Sensing
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• v.14 no.3
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• pp.223-231
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• 1998

Ocean Scanning Multispectral Imager (OSMI) is a payload on the Korean Multi-Purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring for the study of biological oceanography. The instrument images the ocean surface using a whisk-broom motion with a swath width of 800 km and a ground sample distance (GSD) of less than 1 km over the entire field-of-view (FOV). The instrument is designed to have an on-orbit operation duty cycle of 20% over the mission lifetime of 3 years with the functions of programmable gain/offset and on-orbit image data storage. The instrument also performs sun calibration and dark calibration for on-orbit instalment calibration. The OSMI instrument is a multi-spectral imager covering the spectral range from 400 nm to 900 nm using a Charge Coupled Device (CCD) Focal Plane Array (FPA). The ocean colors are monitored using 6 spectral channels that can be selected via ground commands after launch. The instrument performances are fully measured for 8 basic spectral bands centered at 412, 443, 490, 510, 555, 670, 765 and 865 nm during ground characterization of instalment. In addition to the ground calibration, the on-orbit calibration will also be used for the on-orbit band selection. The on-orbit band selection capability can provide great flexibility in ocean color monitoring.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1975-1984
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• 2021

Urban expansion results in raising the temperature in the city, which can cause social, economic and physical damage. In order to prevent the urban heat island and reduce the urban land surface temperature, it is important to quantify the cooling effect of the features of the urban space. Therefore, in order to understand the relationship between each object of land cover and the land surface temperature in Seoul, the land cover map was classified into 6 classes. And the correlation and multiple regression analysis between land surface temperature and the area of objects, perimeter/area, and normalized difference vegetation index was analyzed. As a result of the analysis, the normalized difference vegetation index showed a high correlation with the land surface temperature. Also, in multiple regression analysis, the normalized difference vegetation index exerted a higher influence on the land surface temperature prediction than other coefficients. However, the explanatory power of the derived models as a result of multiple regression analysis was low. In the future, if continuous monitoring is performed using high-resolution MIR Image from KOMPSAT-3A, it will be possible to improve the explanatory power of the model. By utilizing the relationship between such various land cover types considering vegetation vitality of green areas with that of land surface temperature within urban spaces for urban planning, it is expected to contribute in reducing the land surface temperature in urban spaces.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.29-42
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• 2016

Remote sensing allows acquisition of information across a large area without contacting objects, and has thus been rapidly developed by application to different areas. Thus, with the development of remote sensing, satellites are able to rapidly advance in terms of their image resolution. As a result, satellites that use remote sensing have been applied to conduct research across many areas of the world. However, while research on remote sensing is being implemented across various areas, research on data processing is presently insufficient; that is, as satellite resources are further developed, data processing continues to lag behind. Accordingly, this paper discusses plans to maximize the performance of satellite image processing by utilizing the CUDA(Compute Unified Device Architecture) Library of NVIDIA, a parallel processing technique. The discussion in this paper proceeds as follows. First, standard KOMPSAT(Korea Multi-Purpose Satellite) images of various sizes are subdivided into five types. NDVI(Normalized Difference Vegetation Index) is implemented to the subdivided images. Next, ArcMap and the two techniques, each based on CPU or GPU, are used to implement NDVI. The histograms of each image are then compared after each implementation to analyze the different processing speeds when using CPU and GPU. The results indicate that both the CPU version and GPU version images are equal with the ArcMap images, and after the histogram comparison, the NDVI code was correctly implemented. In terms of the processing speed, GPU showed 5 times faster results than CPU. Accordingly, this research shows that a parallel processing technique using CUDA Library can enhance the data processing speed of satellites images, and that this data processing benefits from multiple advanced remote sensing techniques as compared to a simple pixel computation like NDVI.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.376-381
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• 1998

In this study, we was measured the radiance reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the multi-purpose satellite(KOMPSAT) to use the data in analyzing water pollution. Also we investigated the possibility of extraction of water quality factors in rivers and water body by using high resolution remote sensing data such as Airborne MSS. Especially, we tried to extract the environmental factors related with eutrophication, and also tried to develop the process technique and the radiance feature of reflectance related with eutrophication. The results were summarized as follows: First, the spectrum of sun's rays which reaches the surface of the earth was consistent with visible rays bands of 0.4${\mu}{\textrm}{m}$~0.7${\mu}{\textrm}{m}$ and about 50% of total quantity of radiation were there. And at around 0.5${\mu}{\textrm}{m}$ of green spectral band in visible rays bands, the spectrum was highest. Second, as a result of the radiance reflectance Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and suspended sediments and turbidity represented high spectral reflectance at 0.8${\mu}{\textrm}{m}$ and at 0.57${\mu}{\textrm}{m}$ each. Third, as a result of the water quality analysis by using Airborne MSS, Chlorophyll-a could have a distribution chart when carried out ratio of B3 and BS to B7. And Band 7 was useful for making the distribution chart of suspended sediments. And when we carried out PCA, suspended sediments and turbidity had distributions at PC 1 , PC 4 each similarly to ground truth data. Above results can be changed according to the change of season and time. Therefore, in order to analyze more exactly the environmental factors of water quality by using LRC data, we need to investigate constantly the ground truth data and the radiance feature of reflectance of water body. Afterward in this study, we will constantly analyze the radiance feature of the surface of water in water body by measuring the on-the-spot radiance reflectance and using low resolution satellite image(SeaWiFs). Besides, we will gather the data of water quality analysis in water body and analyze the pattern of water pollution.