• Title/Summary/Keyword: KOMPSAT Satellite Image

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Content Analysis-based Adaptive Filtering in The Compressed Satellite Images (위성영상에서의 적응적 압축잡음 제거 알고리즘)

  • Choi, Tae-Hyeon;Ji, Jeong-Min;Park, Joon-Hoon;Choi, Myung-Jin;Lee, Sang-Keun
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.5
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    • pp.84-95
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    • 2011
  • In this paper, we present a deblocking algorithm that removes grid and staircase noises, which are called "blocking artifacts", occurred in the compressed satellite images. Particularly, the given satellite images are compressed with equal quantization coefficients in row according to region complexity, and more complicated regions are compressed more. However, this approach has a problem that relatively less complicated regions within the same row of complicated regions have blocking artifacts. Removing these artifacts with a general deblocking algorithm can blur complex and undesired regions as well. Additionally, the general filter lacks in preserving the curved edges. Therefore, the proposed algorithm presents an adaptive filtering scheme for removing blocking artifacts while preserving the image details including curved edges using the given quantization step size and content analysis. Particularly, WLFPCA (weighted lowpass filter using principle component analysis) is employed to reduce the artifacts around edges. Experimental results showed that the proposed method outperforms SA-DCT in terms of subjective image quality.

Relative RPCs Bias-compensation for Satellite Stereo Images Processing (고해상도 입체 위성영상 처리를 위한 무기준점 기반 상호표정)

  • Oh, Jae Hong;Lee, Chang No
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.4
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    • pp.287-293
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    • 2018
  • It is prerequisite to generate epipolar resampled images by reducing the y-parallax for accurate and efficient processing of satellite stereo images. Minimizing y-parallax requires the accurate sensor modeling that is carried out with ground control points. However, the approach is not feasible over inaccessible areas where control points cannot be easily acquired. For the case, a relative orientation can be utilized only with conjugate points, but its accuracy for satellite sensor should be studied because the sensor has different geometry compared to well-known frame type cameras. Therefore, we carried out the bias-compensation of RPCs (Rational Polynomial Coefficients) without any ground control points to study its precision and effects on the y-parallax in epipolar resampled images. The conjugate points were generated with stereo image matching with outlier removals. RPCs compensation was performed based on the affine and polynomial models. We analyzed the reprojection error of the compensated RPCs and the y-parallax in the resampled images. Experimental result showed one-pixel level of y-parallax for Kompsat-3 stereo data.

Applicability Analysis of Constructing UDM of Cloud and Cloud Shadow in High-Resolution Imagery Using Deep Learning (딥러닝 기반 구름 및 구름 그림자 탐지를 통한 고해상도 위성영상 UDM 구축 가능성 분석)

  • Nayoung Kim;Yerin Yun;Jaewan Choi;Youkyung Han
    • Korean Journal of Remote Sensing
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    • v.40 no.4
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    • pp.351-361
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    • 2024
  • Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

Simulation Approach for the Tracing the Marine Pollution Using Multi-Remote Sensing Data (다중 원격탐사 자료를 활용한 해양 오염 추적 모의 실험 방안에 대한 연구)

  • Kim, Keunyong;Kim, Euihyun;Choi, Jun Myoung;Shin, Jisun;Kim, Wonkook;Lee, Kwang-Jae;Son, Young Baek;Ryu, Joo-Hyung
    • Korean Journal of Remote Sensing
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    • v.36 no.2_2
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    • pp.249-261
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    • 2020
  • Coastal monitoring using multiple platforms/sensors is a very important tools for accurately understanding the changes in offshore marine environment and disaster with high temporal and spatial resolutions. However, integrated observation studies using multiple platforms and sensors are insufficient, and none of them have been evaluated for efficiency and limitation of convergence. In this study, we aimed to suggest an integrated observation method with multi-remote sensing platform and sensors, and to diagnose the utility and limitation. Integrated in situ surveys were conducted using Rhodamine WT fluorescent dye to simulate various marine disasters. In September 2019, the distribution and movement of RWT dye patches were detected using satellite (Kompsat-2/3/3A, Landsat-8 OLI, Sentinel-3 OLCI and GOCI), unmanned aircraft (Mavic 2 pro and Inspire 2), and manned aircraft platforms after injecting fluorescent dye into the waters of the South Sea-Yeosu Sea. The initial patch size of the RWT dye was 2,600 ㎡ and spread to 62,000 ㎡ about 138 minutes later. The RWT patches gradually moved southwestward from the point where they were first released,similar to the pattern of tidal current flowing southwest as the tides gradually decreased. Unmanned Aerial Vehicles (UAVs) image showed highest resolution in terms of spatial and time resolution, but the coverage area was the narrowest. In the case of satellite images, the coverage area was wide, but there were some limitations compared to other platforms in terms of operability due to the long cycle of revisiting. For Sentinel-3 OLCI and GOCI, the spectral resolution and signal-to-noise ratio (SNR) were the highest, but small fluorescent dye detection was limited in terms of spatial resolution. In the case of hyperspectral sensor mounted on manned aircraft, the spectral resolution was the highest, but this was also somewhat limited in terms of operability. From this simulation approach, multi-platform integrated observation was able to confirm that time,space and spectral resolution could be significantly improved. In the future, if this study results are linked to coastal numerical models, it will be possible to predict the transport and diffusion of contaminants, and it is expected that it can contribute to improving model accuracy by using them as input and verification data of the numerical models.

Flood Monitoring and Information System Using Satellite Image (위성영상을 이용한 홍수해 모니터링 및 정보제공체계)

  • Hong, Il-Pyo;Pyeon, Mu-Wook;Kim, Chang-Sik
    • Proceedings of the Korea Water Resources Association Conference
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    • 2006.05a
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    • pp.1226-1230
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    • 2006
  • 위성영상 기술을 이용하여 홍수 발생시 홍수피해지역의 신속한 산출하고, 그 피해내용에 대한 파악을 위성 영상과 그 활용 기술을 이용하여 분석하고자 하였고, 지구관측 위성영상의 홍수방재 분야에서의 기술적 활용성을 살펴보고, 이를 바탕으로 위성영상을 이용하여 홍수피해 범위의 산출 및 피해내용의 산출을 위한 체계적인 공정을 수립하였다. 특히, 기존의 홍수해 방재 업무 중 위성영상을 이용하여 홍수해 분석이 가능한 항목과 범위를 도출하고 비광학 원격탐사의 대표적 기술인 레이더 영상의 분석 기술에 대해서 분석하여 악천후시의 홍수 추적에 활용하도록 정리하였다. 사례연구의 과거 중대 홍수지역 대상유역은 2002년 8월에 발생한 태풍 '루사'로 인해 가장 피해를 많이 입은 강원도 지역의 남대천 및 그 상류지역으로, 해당 지역에 대한 홍수 전후의 변화탐지 분석을 위성영상을 이용하여 수행하였으며, 위성영상 활용 기술의 실용성을 검증하였다. 또한 위성영상 기반 수해정보의 수집.생성 및 제공 체계를 제시하였다. 이러한 연구의 경제적인 측면의 활용성은 재난피해 저감으로 인한 사회비용 절감이라는 측면과 기술적인 측면에서는 아직 개발 초기 단계이지만 점증하고 있는 위성영상의 홍수재해분야 활용에 대한 기술적 기반을 제공에 있다. 본 연구결과는 홍수관리 뿐만 아니라 수자원의 계획 및 관리에도 활용될 것으로 판단되며, 특히 분석항목의 종류에 따라 적절한 영상을 사용하도록 유도함으로써 비용..효과의 측면에서도 중요한 참고 자료로 활용될 수 있다.

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High resolution satellite image classification enhancement using restortation of buildin shadow and occlusion (건물 그림자와 폐색 보정을 통한 고해상도 위성영상의 분류정확도 향상)

  • Kim, Hye-Jin;Han, You-Kyung;Choi, Jae-Wan;Kim, Yong-Il
    • Proceedings of the KSRS Conference
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    • 2009.03a
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    • pp.13-17
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    • 2009
  • 고해상도 위성영상의 분류 기술은 최근 가장 활발히 연구되고 있는 분야 중 하나로 텍스쳐(texture), NDVI, PCA 영상 등 다양한 전처리 정보들을 추출하고 이를 멀티스펙트럴 밴드와 조합하여 분류 정확도를 높이는 기술을 개발하는 연구들이 주를 이루고 있다. 고해상도 위성영상에서 건물의 그림자와 옆벽면의 폐색 지역은 개체 추출 및 분류를 방해하는 주된 요인이 되며, 다양한 형태와 분광특성을 갖는 개개의 건물은 자동 분류 과정을 통해 제대로 식별되지 않는다는 한계를 갖는다. 이에 본 연구에서는 KOMPSAT-2 단영상으로부터 효율적으로 건물 정보 및 토지피복을 분류하기 위하여, 추출된 건물 정보를 바탕으로 건물의 그림자와 폐색지역을 보정한 후 비건물 지역에 대한 분류를 수행하여 분류 정확도를 높이고자 하였다. 우선 삼각벡터구조 기반의 반자동 인터페이스를 이용하여 건물의 3차원 모델 및 그림자 영역을 추출하고 이로부터 추출된 그림자 영역을 효과적으로 보정하기 위해 반복 선형회귀 연산을 이용한 그림자 보정을 수행한 후 inpainting 기법을 건물 폐색영역 복원에 적용하여 영상의 품질을 향상시켰다. 이러한 과정을 통해 도심 지역의 영상 분석에 있어 가장 큰 오차를 일으키는 인공물의 그림자와 폐색에 의한 오차를 최소화한 후 분류에 적용하여 이를 보정 전 영상을 이용한 분류 결과와 비교하였다.

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Characteristics of Ocean Scanning Multi-spectral Imager(OSMI) (Ocean Scanning Multi-spectral Imager (OSMI) 특성)

  • Young Min Cho;Sang-Soon Yong;Sun Hee Woo;Sang-Gyu Lee;Kyoung-Hwan Oh;Hong-Yul Paik
    • 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.

Change Detection Using Deep Learning Based Semantic Segmentation for Nuclear Activity Detection and Monitoring (핵 활동 탐지 및 감시를 위한 딥러닝 기반 의미론적 분할을 활용한 변화 탐지)

  • Song, Ahram;Lee, Changhui;Lee, Jinmin;Han, Youkyung
    • 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.

An Approach to Measurement of Water Quality Factors and its Application Using NOAA satellite Data

  • Jang, Dong-Ho;Jo, Gi-Ho;Chi, Kwang-Hoon
    • Proceedings of the KSRS Conference
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    • 1999.11a
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    • pp.363-370
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    • 1999
  • Remotely sensed data is regarded as a potentially effective data source for the measurement of water quality and for the environmental change of water bodies. In this study, we measured the spectral reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the OSMI multi-purpose satellite(KOMPSAT) scheduled to be launched on 1999 to use the data in analyzing water pollution. We also investigated the possibility of extraction of water quality factors in water bodies by using remotely sensed low resolution data such as NOAA/AVHRR. In this study, Shiwha-District and Sang-Sam Lake was set up as the subject areas for the study. In this part of the study, we measured the spectral reflectance of the water surface to analyze the radiance of the water bodies in low resolution spectral band and tried to analyze the water quality factors in water bodies by using radiance feature from another remotely sensed data such as NOAA/AVHRR. As the method of this study, first, we measured the spectral reflectance of the water surface by using SFOV( Single Field of View) to measure the reflectance of water quality analysis from every channel in LRC spectral band(0.4~O.9${\mu}{\textrm}{m}$). Second, we investigated the usefulness of ground truth data and the LRC data by measuring every spectral reflectance of water quality factors. Third, we analyzed water quality factors by using the radiance feature from another remotely sensed data such as NOAA/AVHRR. We carried out ratio process of what we selected Chlorophyll-a and suspended sediments as the first factors of the water quality. The results of the analysis are below. First, the amount of pollutants of Shiwha-Lake has been increasing every you since 1987 by factors of eutrophication. Second, as a result of the reflectance, Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and turbidity represented high spectral reflectance at 0.57${\mu}{\textrm}{m}$. But suspended sediments absorbed high at 0.8${\mu}{\textrm}{m}$. Third, Chlorophyll-a and suspended sediments could have a distribution chart as a result of the water quality analysis by using NOAA/AVHRR data.

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Shoreline Changes Interpreted from Multi-Temporal Aerial Photographs and High Resolution Satellite Images. A Case Study in Jinha Beach (다중시기 항공사진과 KOMPSAT-3 영상을 이용한 진하해수욕장 해안선 변화 탐지)

  • Hwang, Chang Su;Choi, Chul Uong;Choi, Ji Sun
    • Korean Journal of Remote Sensing
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    • v.30 no.5
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    • pp.607-616
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    • 2014
  • This research is to observe the shoreline changes in Jinha beach over the 50 years with aerial photographs and satellite images. The shoreline image feature was retrieved from the corrected images using wet and dry techniques and analyzed by DSAS from the statistical point of view. From 1967 to 1992, the mouth of Hoeya River was severely blocked and the northern shoreline off Jinha beach was eroded. The blockade of river mouth seemed to have been eased along with the completion of the dike, but soil continued to be deposited along the high sea away from the river month. Compared to the past, a layer of sediment has been formed off the northern coastline while the southern coastline has eroded. At least in the region subject to this research, the construction of a training dike is to blame. On top of that, a mere combination of dredges and artificial nourishment is not enough to take under control the changing shorelines properly. Thus, it is necessary to devise a more fundamental solution by taking into account reasons behind sediment from the river area that could change the shorelines besides the costal environment.