• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.31-34
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• 2009

본 연구에서는 KOMPSAT-3급 고해상도 위성영상을 이용하여 전처리 후 정밀 농업 주제정보를 추출하는 방법론을 제시하고자 하였다. 분석에 사용한 KOMPSAT-3급 고해상도 위성영상은 IKONOS (2001/5/25, 2001/12/25, 2003/10/23) 3개의 영상, QuickBird (2006/5/1, 2004/11/17) 2개의 영상, KOMPSAT-2 (2007/9/17) 1개의 영상 등 모두 6개의 영상을 확보 및 각각에 대한 현장 GCP자료 및 RPC, RPB 자료를 수집하여 정사보정을 실시하였다. RMSE는 약 $0.12\sim3.18$의 값으로 분포되었다. KOMPSAT근 급 영상자료로 부터 정밀농업물재배지도를 작성하기 위해 각 벤드별 Scatter기법을 이용하여 각 밴드간의 상간관계를 살펴보고, 3개의 최적의 밴드를 선정하였다. 또한 작물별 최적의 밴드 결정을 위해 각 밴드별 픽셀 값을 사용하여 Texture 분석을 실시하였다. 그 결과 논의 경우 모든 밴드에서 분석이 용이 한 것으로 분석되었으며, 4밴드의 경우 3개의 작물(고추, 옥수수, 벼)의 분석시 매우 적합한 밴드인 것으로 분석되었다. 각 영상별 필터링 기법과, ISODATA 방법을 이용한 정밀농업 토지이용도 작성하여 기존 스크린 디지타이징 기법으로 작성한 정밀토지이용도와 비교하였다. 다양한 식생정보를 추출하는 위하여 확보된 영상자료로부터 RVI, NDVI, ARVI, SAVI 식생지수 를 추출하였으며, 그 결과를 현장자료로부터 추출한 식생지수간의 결과 값과 비교분석하였다.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.411-421
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• 2009

This study refers to develop a semi-automatic extraction of agricultural land use and vegetation information using high resolution satellite images. Data of IKONOS-2 satellite images (May 25 of 2001, December 25 of 2001, and October 23 of 2003), QuickBird-2 satellite images (May 1 of 2006 and November 17 of 2004) and KOMPSAT-2 satellite image (September 17 of 2007) which resemble with the spatial resolution and spectral characteristics of KOMPSAT-3 were used. The precise agricultural land use classification was tried using ISODATA unsupervised classification technique, and the result was compared with on-screen digitizing land use accompanying with field investigation. For the extraction of crop growth information, three crops of paddy, com and red pepper were selected, and the spectral characteristics were collected during each growing period using ground spectroradiometer. The vegetation indices viz. RVI, NDVI, ARVI, and SAVI for the crops were evaluated. The evaluation process was developed using the ERDAS IMAGINE Spatial Modeler Tool.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.21-35
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• 2009

KOMPSAT-2 is the seventh high-resolution image satellite in the world that provides both 1m-grade panchromatic images of the GSD and 4m-grade multispectral images of the GSD. It's anticipated to be used across many different areas including mapping, territory monitoring and environmental watch. However, due to the complexity and security concern involved with the use of the MSC, the use of KOMPSAT-2 images are limited in terms of geometric images, such as satellite orbits and detailed mapping information. Therefore, this study aims to produce DEM and orthoimage by using the stereo images of KOMPSAT-2, and to explore the applicability of geo-spatial information with KOMPSAT -2. Orientation interpretations were essential for the production of DEM and orthoimage using KOMPSAT-2 images. In the study, they are performed by utilizing both RPC and GCP. In this study, the orientation interpretations are followed by the generation of DEM and orthoimage, and the analysis of their accuracy based on a 1:5,000 digital map. The accuracy analysis of DEM is performed and the results indicate that their altitudes are, in general, higher than those obtained from the digital map. The altitude discrepancies on plains, hills and mountains are calculated as 1.8m, 7.2m, and 11.9m, respectively. In this study, the mean differences between horizontal position between the orthoimage data and the digital map data are found to be ${\pm}3.081m$, which is in the range of ${\pm}3.5m$, within the permitted limit of a 1:5,000 digital map. KOMPSAT-2 images are used to produce DEM and orthoimage in this research. The results suggest that DEM can be adequately used to produce digital maps under 1:5,000 scale.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1003-1017
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• 2017

Multispectral image data of high spatial resolution is required to obtain correct information on the ground surface. The multispectral image data has lower resolution compared to panchromatic data. PAN-sharpening fusion technique produces the multispectral data with higher resolution of panchromatic image. Recently the object-based approach is more applied to the high spatial resolution data than the conventional pixel-based one. For the object-based image analysis, it is necessary to perform image segmentation that produces the objects of pixel group. Image segmentation can be effectively achieved by the process merging step-by-step two neighboring regions in RAG (Regional Adjacency Graph). In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. This degradation increases variation of pixel values in same area, and results in deteriorating the accuracy of image segmentation. An iterative approach that reduces the difference of pixel values in two neighboring pixels of same area is employed to alleviate variation of pixel values in same area. The size of segmented regions is associated with the quality of image segmentation and is decided by a stopping rue in the merging process. In this study, the image restoration and segmentation was quantitatively evaluated using simulation data and was also applied to the three PAN-sharpened multispectral images of high resolution: Dubaisat-2 data of 1m panchromatic resolution from LA, USA and KOMPSAT3 data of 0.7m panchromatic resolution from Daejeon and Chungcheongnam-do in the Korean peninsula. The experimental results imply that the proposed method can improve analytical accuracy in the application of remote sensing high resolution PAN-sharpened multispectral imagery.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1391-1401
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• 2019

The KOMPSAT-3A is a high-resolution optical satellite launched in 2015 by Korea Aerospace Research Institute (KARI). KOMPSAT-3A provides Panchromatic (PAN-0.55 m), Multispectral (MS-2.2 m), and Mid-wavelength infrared (MIR_OR-5.5 m) image. However, due to security or military problems, MIR_OR image with 5.5m spatial resolution are provided down sampled at 33 m spatial resolution (MIR_rd). In this study, we propose spatial sharpening method to improve the spatial resolution of MIR_rd image (33 m) using virtual High Frequency (HF) image and optimal fusion factor. Using MS image and MIR_rd image, we generated virtual high resolution (5.5 m) MIR_OR^fus image and then compared them to actual high-resolution MIR_OR image. The test results show that the proposed method merges the spatial resolution of MS image and the spectral information of MIR_rd image efficiently.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.917-927
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• 2020

The Ministry of Land, Infrastructure and Transport of Korea is developing a CAS 500-1/2 satellite capable of photographing a GSD 0.5 m level image, and is developing a technology to utilize this. Therefore, this study attempted to develop a geospatial feature extraction technique aimed at automation as a technique for utilizing CAS 500-1/2 satellite images. KOMPSAT-3A satellite images that are expected to be most similar to CAS 500-1/2 were used for research and the possibility of automation of geospatial feature extraction was analyzed through relative radiometric normalization. For this purpose, the parameters and thresholds were applied equally to the reference images and relative radiometric normalized images, and the geospatial feature were extracted. The qualitative analysis was conducted on whether the extracted geospatial feature is extracted in a similar form from the reference image and relative radiometric normalized image. It was also intended to analyze the possibility of automation of geospatial feature extraction by quantitative analysis of whether the classification accuracy satisfies the target accuracy of 90% or more set in this study. As a result, it was confirmed that shape of geospatial feature extracted from reference image and relative radiometric normalized image were similar, and the classification accuracy analysis results showed that both satisfies the target accuracy of 90% or more. Therefore, it is believed that automation will be possible when extracting spatial objects through relative radiometric normalization.