DOI QR코드

DOI QR Code

An Efficient Data Processing Method to Improve the Geostationary Ocean Color Imager (GOCI) Data Service

천리안 해양관측위성의 배포서비스 향상을 위한 자료 처리 효율화 방안 연구

  • Received : 2013.12.30
  • Accepted : 2014.01.24
  • Published : 2014.02.28

Abstract

We proposed and verified the methods to maintain data qualities as well as to reduce data volume for the Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in geostationary orbit. For the GOCI level-2 data, 92.9% of data volume could be saved by only the data compression. For the GOCI level-1 data, however, just 20.7% of data volume could be saved by the data compression therefore another approach was required. First, we found the optimized number of bits per a pixel for the GOCI level-1 data from an idea that the quantization bit for the GOCI (i.e. 12 bit) was less than the number of bits per a pixel for the GOCI level-1 data (i.e. 32 bit). Experiments were conducted using the $R^2$ and the Modulation Transfer Function (MTF). It was quantitatively revealed that the data qualities were maintained although the number of bits per a pixel was reduced to 14. Also, we performed network simulations using the Network Simulator 2 (Ns2). The result showed that 57.7% of the end-toend delay for a GOCI level-1 data was saved when the number of bits per a pixel was reduced to 14 and 92.5% of the end-to-end delay for a GOCI level-2 data was saved when 92.9% of the data size was reduced due to the compression.

세계 최초의 정지궤도 해양관측위성인 천리안 해양관측위성(Geostationary Ocean Color Imagers;GOCI)의 자료 품질을 유지하면서도 용량을 줄임으로써 자료 배포 서비스를 향상시키기 위한 방법을 검토하였다. 레벨-2 자료는 압축만으로도 약 92.9%의 용량 절약을 할 수 있지만, 레벨-1 자료는 압축으로 인한 용량 절약이 약 20.7%에 그치기 때문에 또 다른 접근 방식이 필요하였다. 이 연구에서는 위성 영상 자료의 양자화 비트 수(12비트)가 레벨-1 자료의 픽셀 당 비트 수(32비트)보다 작게 설정되어 있다는 점에 착안하여, 레벨-1 자료에 대해 최적화된 픽셀 당 비트 수를 찾고자 하였다. $R^2$와 변조전달함수(Modulation Transfer Function, MTF)를 이용한 실험 결과, 레벨-1 자료의 픽셀 당 비트 수를 14비트까지 줄이더라도 위성 영상자료의 품질 저하가 없다는 것을 정량적으로 확인하였다. 또한, Ns2 (Network Simulator 2)를 이용한 네트워크 평가 결과, 레벨-1 자료의 경우 픽셀 당 비트 수를 14비트까지 줄였을 때 배포시간을 약 57.7% 줄일 수 있었고, 레벨-2 자료의 경우 압축을 이용하여 92.9%까지 파일 크기를 줄였을 때 배포시간을 약 92.5% 줄일 수 있었다.

Keywords

References

  1. Ahn, J.H., Y.J. Park, J.H. Ryu, B. Lee, and I.S. Oh, 2012. Development of Atmospheric Correction Algorithm for Geostationary Ocean Color Imager (GOCI). Ocean Science Journal, 47: 247-259. https://doi.org/10.1007/s12601-012-0026-2
  2. Cho, S.I., Y.H. Ahn, J.H. Ryu, G.S. Kang, and H.S. Youn, 2010. Development of Geostationary Ocean Color Imager (GOCI), Korean Journal of Remote Sensing, 26(2): 157-165 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2010.26.2.157
  3. Choi, J.K., Y.J. Park, J.H. Ahn, H.S. Lim, J. Eom, and J.H. Ryu, 2012. GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity. Journal of Geophysical Research-Oceans, 117.
  4. Choi, J.K., H. Yang, H.J. Han, J.H. Ryu, and Y.J. Park, 2013. Quantitative estimation of the suspended sediment movements in the coastal region using GOCI. Journal of Coastal Research, SI: 1367-1372.
  5. Davis, L.S., 1975. A survey of edge detection techniques. Computer Graphics and Image Processing, 4: 248-270. https://doi.org/10.1016/0146-664X(75)90012-X
  6. Han, H.J., J.H. Ryu, and Y.H. Ahn, 2010. Development the Geostationary Ocean Color Imager (GOCI) Data Processing System (GDPS). Korean Journal of Remote Sensing, 26(2): 239-249 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2010.26.2.239
  7. Jeon, B.I., H. Kim, and Y.K. Chang, 2012. A MTF Compensation for Satellite Image Using Lcurve-based Modified Wiener Filter. Korean Journal of Remote Sensing, 28(5): 561-571 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.5.8
  8. Oh, E., S.W. Kim, S.I. Cho, J.H. Ryu, and Y.H. Ahn, 2012. Initial On-Orbit Modulation Transfer Function Performance Analysis for Geostationary Ocean Color Imager. Journal of Astronomy and Space Sciences, 29: 199-208. https://doi.org/10.5140/JASS.2012.29.2.199
  9. Oh, E., K.B. Ahn, S.I. Cho, and J.H. Ryu, 2013. A Modulation Transfer Function Compensation for the Geostationary Ocean Color Imager (GOCI) Based on the Wiener Filter. Journal of Astronomy and Space Sciences, 30(4): 321-326. https://doi.org/10.5140/JASS.2013.30.4.321
  10. Ryu, J.H., H.J. Han, S. Cho, Y.J. Park, and Y.H. Ahn, 2012. Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS). Ocean Science Journal, 47: 223-233. https://doi.org/10.1007/s12601-012-0024-4
  11. Yang, H., J.H. Ahn, H.J. Han, J.H. Ryu, and Y.J. Park, 2012. How Cloud-Free Areas Increase with Eight Times Observations of Geostationary Ocean Color Imager. International Symposium on Remote Sensing (ISRS 2012).
  12. Yang, H., J.M. Ryu, H.J. Han, J.H. Ryu, and Y.J. Park, 2012. Ocean Disaster Detection System (OD2S) using Geostationary Ocean Color Imager (GOCI). Journal of the Korea society of IT services, 11: 177-189 (in Korean with English abstract). https://doi.org/10.9716/KITS.2012.11.sup.177
  13. Yoon, J.E., J. Park, and S. Yoo, 2012. Comparison of primary productivity algorithms for Korean waters. Ocean Science Journal, 47: 473-487. https://doi.org/10.1007/s12601-012-0043-1

Cited by

  1. Comparative Analysis of GOCI Ocean Color Products vol.15, pp.10, 2015, https://doi.org/10.3390/s151025703
  2. 천리안해양관측위성을 위한 자료 처리 시스템 vol.23, pp.1, 2014, https://doi.org/10.5626/ktcp.2017.23.1.74
  3. 서버가상화 및 분산처리를 이용한 천리안해양관측위성 산출물 재처리 시스템 vol.33, pp.2, 2017, https://doi.org/10.7780/kjrs.2017.33.2.2