• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.861-866
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• 2020

CAS(Compact Advanced Satellite) 500-1 satellite and its follow-up, CAS 500-2, are scheduled to be launched in 2021. For these satellites, a research project on 'CAS 500-1/2 Image Acquisition and Utilization Technology Development' has been carried out. This paper summarizes publications carried out under the project, papers presented within this special issue and contributions of the project.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.103-114
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• 2024

The escalating demands for high-resolution satellite imagery necessitate the dissemination of geospatial data with superior accuracy.Achieving precise positioning is imperative for mitigating geometric distortions inherent in high-resolution satellite imagery. However, maintaining sub-pixel level accuracy poses significant challenges within the current technological landscape. This research introduces an approach wherein upsampling is employed on both the satellite image and ground control points (GCPs) chip, facilitating the establishment of a high-resolution satellite image precision sensor orientation. The ensuing analysis entails a comprehensive comparison of matching performance. To evaluate the proposed methodology, the Compact Advanced Satellite 500-1 (CAS500-1), boasting a resolution of 0.5 m, serves as the high-resolution satellite image. Correspondingly, GCP chips with resolutions of 0.25 m and 0.5 m are utilized for the South Korean and North Korean regions, respectively. Results from the experiment reveal that concurrent upsampling of satellite imagery and GCP chips enhances matching performance by up to 50% in comparison to the original resolution. Furthermore, the position error only improved with 2x upsampling. However,with 3x upsampling, the position error tended to increase. This study affirms that meticulous upsampling of high-resolution satellite imagery and GCP chips can yield sub-pixel-level positioning accuracy, thereby advancing the state-of-the-art in the field.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.291-304
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• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of Astronomy and Space Sciences
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• v.39 no.3
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• pp.117-126
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• 2022

The Ionospheric Anomaly Monitoring by Magnetometer And Plasma-probe (IAMMAP) is one of the scientific instruments for the Compact Advanced Satellite 500-3 (CAS 500-3) which is planned to be launched by Korean Space Launch Vehicle in 2024. The main scientific objective of IAMMAP is to understand the complicated correlation between the equatorial electro-jet (EEJ) and the equatorial ionization anomaly (EIA) which play important roles in the dynamics of the ionospheric plasma in the dayside equator region. IAMMAP consists of an impedance probe (IP) for precise plasma measurement and magnetometers for EEJ current estimation. The designated sun-synchronous orbit along the quasi-meridional plane makes the instrument suitable for studying the EIA and EEJ. The newly-devised IP is expected to obtain the electron density of the ionosphere with unprecedented precision by measuring the upper-hybrid frequency (f_UHR) of the ionospheric plasma, which is not affected by the satellite geometry, the spacecraft potential, or contamination unlike conventional Langmuir probes. A set of temperature-tolerant precision fluxgate magnetometers, called Adaptive In-phase MAGnetometer, is employed also for studying the complicated current system in the ionosphere and magnetosphere, which is particularly related with the EEJ caused by the potential difference along the zonal direction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.211-218
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• 2019

The Ministry of Land, Infrastructure and Transport is planning to launch CAS-500 (Compact Advanced Satellite 500) 1 and 2 in 2019 and 2020. Satellite image information collected through CAS-500 can be used in various fields such as global environmental monitoring, topographic map production, analysis for disaster prevention. In order to utilize in various fields like this, it is important to get the location accuracy of the satellite image. In order to establish the precise geometry of the satellite image, it is necessary to establish a precise sensor model using the GCP (Ground Control Point). In order to utilize various fields, step - by - step automation for orthoimage construction is required. To do this, a database of satellite image GCP chip should be structured systematically. Therefore, in this study, we will analyze various techniques for automatic GCP extraction for precise geometry of satellite images.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.311-326
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• 2022

The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.343-350
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• 2024

Since the release of Meta's Segment Anything Model (SAM), a large-scale vision transformer generation model with rapid image segmentation capabilities, several studies have been conducted to apply this technology in various fields. In this study, we aimed to investigate the applicability of SAM for water bodies detection and extraction using the QGIS Geo-SAM plugin, which enables the use of SAM with satellite imagery. The experimental data consisted of Compact Advanced Satellite 500 (CAS500)-1 images. The results obtained by applying SAM to these data were compared with manually digitized water objects, Open Street Map (OSM), and water body data from the National Geographic Information Institute (NGII)-based hydrological digital map. The mean Intersection over Union (mIoU) calculated for all features extracted using SAM and these three-comparison data were 0.7490, 0.5905, and 0.4921, respectively. For features commonly appeared or extracted in all datasets, the results were 0.9189, 0.8779, and 0.7715, respectively. Based on analysis of the spatial consistency between SAM results and other comparison data, SAM showed limitations in detecting small-scale or poorly defined streams but provided meaningful segmentation results for water body classification.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.2
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• pp.155-166
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• 2021

In this paper, design study of a small two-stage launch vehicle is undertaken for the dedicated launch of the Compact Advanced Satellite 500 (CAS500)-class satellite into the Low Earth Orbit (LEO) by modifying the second and third stages of the Korean Space Launch Vehicle II (KSLV-II). Since the KSLV-II has three stages, velocity increment is newly distributed for the two-stage small launch vehicle. For this end, the staging design is carried out for the design parameters such as stage mass ratios, structural coefficients and engine options for each stage followed by trajectory analysis. Investigation of the results provides the combination of design parameters for the small launch vehicle for the dedicated launch of 500 kg-class satellite into LEO.