• Journal of Korean Society for Geospatial Information Science
• /
• v.20 no.1
• /
• pp.53-63
• /
• 2012

As a result of the economical loss caused by unusual climate changes resulting from emission of excessive green house gases such as carbon dioxide which is expected to account for 5~20% of the world GDP by 2100, researching technologies regarding the reduction of carbon dioxide emission is being favored worldwide as a part of the high value-added industry. As one of the Annex II countries of Kyoto Protocol of 1997 that should keep the average $CO_2$ emission rate of 5% by 2013, South Korea is also dedicated to the researches and industries of $CO_2$ emission reduction. In this study, Application of LiDAR data & KOMPSAT-2 satellite image for calculated forest Biomass. Raw LiDAR data's tree numbers and tree-high with field survey data resulted in 90% similarity of objects and an average of 0.3m difference in tree-high. Calculating the forest biomass through forest type information categorized as KOMPSAT-2 image and LiDAR data's tree-high data of tree enabled the estimation of $CO_2$ absorption and forest biomass of forest type, The similarity between the field survey average of 90% or higher were analyzed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.24 no.10
• /
• pp.1001-1007
• /
• 2013

In the case of multi-mission LEO(Low Earth Orbit) operations, depending on the orbit of each satellite, one ground site is supposed to be communicated with more than two satellites at the same time. On top of that, image data processing system is generally mission-specific and 1:1 backup configuration. For the reason, if ground site has smaller number of antenna than that of satellite, interface with image data processing system would be very complicated. In this paper, considering that two LEO satellites can be operating and image data recording unit in redundancy can be easily plug-in, the implementation of matrix receiving structure is described. This matrix receiving structure has been validated from KOMPSAT-2 and -3(KOrea Multi-Purpose SATellite-2 and -3) since KOMPSAT-3 was launched in May, 2012. This structure will be applied for the KOMPSAT-3A and -5 through its expandability.

• Journal of The Geomorphological Association of Korea
• /
• v.19 no.3
• /
• pp.37-49
• /
• 2012

The objective of this paper is to propose a methodology for surface sediments classification in tidal flats that can combine ground survey data with high-resolution remote sensing data by multivariate kriging. Unlike conventional methodologies that have classified remote sensing data by using pre-classified sediment components, a new classification methodology presented in this paper first generates sediment component fraction maps and then classifies the sediments on a final stage. For generating sediment component fractions, regression kriging, as one of multivariate kriging algorithms, is applied to integrate ground survey data and remote sensing data. First, trend components of sand, silt, and clay are derived through regression analysis of ground survey data and spectral information from remote sensing data. Then, residuals at sample locations are computed and interpolated to generate residual components in the study area. Finally, the sediment component fractions are computed by adding the residuals to the trend components and are classified on a final stage. A case study at the Baramarae tidal flats with KOMPSAT-2 imagery is carried out to evaluate the classification capability of the proposed classification methodology. Through the case study, the proposed methodology showed the best classification accuracy, compared with the conventional classification methodologies. Especially, much improvement of classification accuracy for fine-grained sediments were also obtained. Therefore, it is expected that the presented classification methodology would be an effective one for surface sediments classification in tidal flats.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_4
• /
• pp.1879-1887
• /
• 2022

In this study, a method of estimating the velocity and altitude of aircrafts photographed in a KOMPSAT-3 satellite was proposed. In the proposed method, parallax effect, which is a time offset between bands due to the photographing method of the KOMPSAT-3 satellite, the structure of the sensor, and the movement of the satellite's orbit, was calculated, and in this process, trucks running on the highway were used. In addition, the actual direction and the direction by parallax effect of the aircraft were calculated using the coordinates of the aircraft in the image, and the attitude information of the KOMPSAT-3 satellite was calculated using metadata to estimate the velocity and altitude of the aircraft. The estimated value through the proposed method was compared with the actual value, automatic dependent surveillance-broadcast (ADS-B), and the error rate was calculated here. As a result, it was confirmed that the velocity and altitude error rate of large aircraft (I1, I3, S2) were lower than that of light aircraft (I2, S2), and the estimated velocity and altitude were relatively high in large aircraft using the proposed method.

• Korean Journal of Remote Sensing
• /
• v.30 no.5
• /
• pp.547-557
• /
• 2014

This paper presents a method for designing circular template used in similarity measurement for image registration. Circular template has translation and rotation invariant property, which results in correct matching of control points for image registration under the condition of translation and rotation between reference and sensed images. Circular template consisting of the pixels located on the multiple circumferences of the circles whose radii vary from zero to a certain distance, is converted to two-dimensional Discrete Polar Coordinate Matrix (DPCM), whose elements are the pixels of the circular template. For sensed image, the same type of circular template and DPCM are created by rotating the circular template repeatedly by a certain degree in the range between 0 and 360 degrees and then similarity is calculated using mutual information of the two DPCMs. The best match is determined when the mutual information for each rotation angle at each pixel in search area is maximum. The proposed algorithm was tested using KOMPSAT-2 images acquired at two different times and the results indicate high accurate matching performance under image rotation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.1
• /
• pp.1-9
• /
• 2011

The objective of this paper is to improve the accuracy of the 3D geopositioning extracted from Rational Polynomial Coefficient(RPC) provide in the KOMPSAT-2 metadata files. In this paper, we developed the algorithm to adjust a RFM(Rational Functional Model), and could improve the accuracy of a RFM with this algorithm. Furthermore, when a RFM was adjusted with this algorithm, the effects of the number of GCPs on the accuracy of the adjusted RFM was tested. For accuracy assessment using adjusted RFM, 9 ground control points(GCPs) and 24 check points could be used. Results indicated that the root mean squared errors(RMSEs) of horizontal residual errors calculated 24 check points were 2.20(m). The achieved accuracy of three dimensional object-point determination was 1.72(m) in the X-dimension and 1.37(m) in the Y-dimension and 2.20(m) in the Z-dimension.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.101-107
• /
• 2013

Generally the commercial high-resolution images have their coordinates, but the locations are locally different according to the pose of sensors at the acquisition time and relief displacement of terrain. Therefore, a process of image co-registration has to be applied to use the multi-temporal images together. However, co-registration is interrupted especially when images include the cloud-covered regions because of the difficulties of extracting matching points and lots of false-matched points. This paper proposes an automatic co-registration method for the cloud-covered high-resolution images. A scale-invariant feature transform (SIFT), which is one of the representative feature-based matching method, is used, and only features of the target (cloud-covered) images within a circular buffer from each feature of reference image are used for the candidate of the matching process. Study sites composed of multi-temporal KOMPSAT-2 images including cloud-covered regions were employed to apply the proposed algorithm. The result showed that the proposed method presented a higher correct-match rate than original SIFT method and acceptable registration accuracies in all sites.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_3
• /
• pp.1707-1720
• /
• 2023

With the increase in satellite time series data, the utility of remote sensing data is growing. In the analysis of time series data, the relative positional accuracy between images has a significant impact on the results, making image registration essential for correction. In recent years, research on image registration has been increasing by applying deep learning, which outperforms existing image registration algorithms. To train deep learning-based registration models, a large number of image pairs are required. Additionally, creating a correlation map between the data of existing deep learning models and applying additional computations to extract registration points is inefficient. To overcome these drawbacks, this study developed a data augmentation technique for training image registration models and applied it to OffsetNet, a registration model that predicts the offset amount itself, to perform image registration for KOMSAT-2, -3, and -3A. The results of the model training showed that OffsetNet accurately predicted the offset amount for the test data, enabling effective registration of the master and slave images.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_2
• /
• pp.1523-1535
• /
• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.3
• /
• pp.125-132
• /
• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.