[논문]RPC를 기반으로 한 아리랑 2호 에피폴라 영상제작

오재홍; 이효성

doi:10.7848/ksgpc.2011.29.2.157

RPC를 기반으로 한 아리랑 2호 에피폴라 영상제작
RPC-based epipolar image resampling of Kompsat-2 across-track stereos 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.29 no.2, 2011년, pp.157 - 164

오재홍 (Civil and Environmental Engineering and Geodetic Science the Ohio State University) , 이효성 (Civil Engineering Suncheon National University)

Abstract ▼ AI-Helper

As high-resolution satellite images have enabled large scale topographic mapping and monitoring on global scale with short revisit time, agile sensor orientation, and large swath width, many countries make effort to secure the satellite image information. In Korea, KOMPSAT-2 (KOrea Multi-Purpose SATellite-2) was launched in July 28 2006 with high specification. These satellites have stereo image acquisition capability for 3D mapping and monitoring. To efficiently handle stereo images such as stereo display and monitoring, the accurate epipolar image generation process is prerequisite. However, the process was highly limited due to complexity in epipolar geometry of pushbroom sensor. Recently, the piecewise approach to generate epipolar images using RPC was developed and tested for in-track IKONOS stereo images. In this paper, the piecewise approach was tested for KOMPSAT-2 across-track stereo images to see how accurately KOMPSAT-2 epipolar images can be generated for 3D geospatial applications. In the experiment, two across-track stereo sets from three KOMPSAT-2 images of different dates were tested using RPC as the sensor model. The test results showed that one-pixel level of y-parallax was achieved for manually measured tie points.

주제어

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

문제 정의

(2010). The authors showed the approximate existence of the epipolar curve paiis for the entire scene based on the proposed method. The epipolar image resampling algorithm consists of two main steps; the epipolar curve point generation over the entire scene, and the establishment of epipolar image transformation.

제안 방법

First, the accuracy of KOMPSAT-2 RPC was improved up to near one-pixel level using well-distributed ground controls. Second, the newly developed piecewise method was applied to the two stereo sets. Then, accurate epipolar image generation was carried out.
(2010) to accurately determine epipolar geometry. The method is based on iterative projections from an image to the ground, and from the ground to the paired image. The projection can be carried out based on the rigorous physical sensor models or RPC (Rational Polynomial Coefficients) which shows little difference compared to the physical sensor model.
KOMPSAT-2 acquires stereo images in across-track, and it also requires accurate epipolar curve determination and image resampling. Therefore, this study applied the piecewise approach to KOMPSAT-2 across-track stereo images to see how accurately the epipolar image resampling can be carried out by the method.

대상 데이터

Table 1 presents the specification of each tested image. The target area is Daegu, Korea which location is about 35.89°N and 128.49°E, and the terrain elevation range is about 400 meters. Note that each image was acquired in different date and trajectory, i.

이론/모형

KOMP- SAT-2 also can acquire stereo images in across-track, and it requires the accurate epipolar image resampling to be used for 3D geospatial applications. Therefore, this study tested the recently developed piecewise method to KOMPSAT-2 across-track stereo images to see how accurately epipolar images can be generated.
Three KOMPSAT-2 images were tested for the piecewise method. Table 1 presents the specification of each tested image.

성능/효과

Then, accurate epipolar image generation was carried out. The test results clearly showed that the epipolar image generation was successful showing one-pixel level of y-parallax, which was introduced by RPC accuracy and observation accuracy of tie points. Even though the stereo sets do not have decent convergence angle, the ground restitution accuracy was one meter level in horizontal and two or three meters in vertical.

참고문헌 (13)

Dial, G. and Grodecki J. (2002), Block adjustment with rational polynomial camera models, Proceedings of the ACSMASPRS 2002 Annual Conference, ASPRS, Washington DC, unpaginated CD-ROM.
Fraser, C.S. and Hanley, H.B. (2005), Bias-compensated RPC for sensor orientation of high-resolution satellite imagery, Photogrammetric Engineering & Remote Sensing, ASPRS, Vol. 71, No. 8, pp. 909-915.

상세보기
Fraser, C.S. and Ravanbakhsh, M. (2009), Georeferencing accuracy of GeoEye-1 imagery, Photogrammetric Engineering & Remote Sensing, ASPRS, Vol. 75, No. 6, pp. 634-638.

상세보기
Grodecki, J. (2001), IKONOS stereo feature extraction - RPC approach, Proceedings of ASPRS 2001 Annual Convention, ASPRS, St. Louis, Missouri, unpaginated CD-ROM.
Grodecki, J., Dial, G. and Lutes, J. (2004), Mathematical model for 3D feature extraction from multiple satellite images described by RPC. Proceedings of the ASPRS 2004 Annual Conference, ASPRS, Denver, Colorado, unpaginated CD-ROM.
Gupta, R. and Hartley, R. (1997), Linear Pushbroom Cameras, IEEE Transactions on pattern analysis and machine intelligence, IEEE TPAMI, Vol. 19, No. 9, pp. 963-975.

상세보기
Kim, T. (2000), A Study on the epipolarity of linear pushbroom images, Photogrammetric Engineering & Remote Sensing, ASPRS, Vol. 66, No. 8, pp. 961-966.

상세보기
Morgan, M. (2004), Epipolar resampling of linear array scanner scene, PhD dissertation, University of Calgary, Calgary, Alberta.
Oh, J. H., Shin, S.W. and Kim, K. (2006), Direct Epipolar Image generation From IKONOS Stereo Imagery Based on RPC and Parallel Projection Model, Korean Journal of Remote Sensing, KSRS, Vol. 22, No. 5, pp. 451-456.

원문보기 상세보기
Oh, J. H., Lee, W.H., Toth, C.K., Grejner-Brzezinska, D.A. and Lee, C.N. (2010), A Piecewise Approach to Epipolar Resampling of Pushbroom Satellite Images Based on RPC, Photogrammetric Engineering & Remote Sensing, ASPRS, Vol. 76, No. 12, pp. 1353-1363.

상세보기
Ono, T. (1999), Epipolar Resampling of High resolution satellite imagery, International Archives of Photogrammetry and Remote Sensing, the Joint Workshop of ISPRS WG I/1, I/3 and IV/4 on Sensors and Mapping from Space, ISPRS, Hannover, unpaginated CD-ROM.
Seo, D.C., Yang, J. Y., Lee, D. H., Song, J. H. and Lim, H. S. (2008), Kompsat-2 Direct Sensor Modeling and Geometric Calibration/Validation, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. ISPRS, Beijing, Vol. XXXVII. Part B1.
Stoney, W. E. (2008), ASPRS Guide to Land Imaging Satellites, http://www.asprs.org/news/satellites/satellites.html.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

RPC를 기반으로 한 아리랑 2호 에피폴라 영상제작
RPC-based epipolar image resampling of Kompsat-2 across-track stereos 원문보기

Abstract ▼ AI-Helper

주제어

AI 본문요약
AI-Helper

문제 정의

제안 방법

대상 데이터

이론/모형

성능/효과

참고문헌 (13)

이 논문을 인용한 문헌

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

RPC를 기반으로 한 아리랑 2호 에피폴라 영상제작 RPC-based epipolar image resampling of Kompsat-2 across-track stereos 원문보기

Abstract ▼ AI-Helper

주제어

AI 본문요약 엑셀 다운로드 AI-Helper

문제 정의

제안 방법

대상 데이터

이론/모형

성능/효과

참고문헌 (13)

이 논문을 인용한 문헌

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

RPC를 기반으로 한 아리랑 2호 에피폴라 영상제작
RPC-based epipolar image resampling of Kompsat-2 across-track stereos 원문보기

AI 본문요약
AI-Helper