Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Comparative Analysis of Target Detection Algorithms in Hyperspectral Image

초분광영상에 대한 표적탐지 알고리즘의 적용성 분석

  • Shin, Jung-Il (Department of Geoinformatic Engineering, Inha University) ;
  • Lee, Kyu-Sung (Department of Geoinformatic Engineering, Inha University)
  • 신정일 (인하대학교 지리정보공학과) ;
  • 이규성 (인하대학교 지리정보공학과)
  • Received : 2012.06.21
  • Accepted : 2012.07.08
  • Published : 2012.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, many target detection algorithms were developed for hyperspectral image. However, almost of these studies focused only accuracy from 1 or 2 data sets to validate and compare the algorithms although they give limited information to users. This study aimed to compare usability of target detection algorithms with various parameters. Five parameters were proposed to compare sensitivity in aspect of detection accuracy which are related with radiometric and spectral characteristics of target, background and image. Six target detection algorithms were compared in aspect of accuracy and efficiency (processing time) by variation of the parameters and image size, respectively. The results shown different usability of each algorithm by each parameter in aspect of accuracy. Second order statistics based algorithms needed relatively long processing time. Integrated usabilities of accuracy and efficiency were various by characteristics of target, background and image. Consequently, users would consider appropriate target detection algorithms by characteristics of data and purpose of detection.

현재까지 초분광영상을 위한 다양한 표적탐지 알고리즘이 개발 및 사용되고 있다. 그러나 표적탐지 알고리즘의 비교 및 검증 기준으로 1~2가지 영상에 적용한 탐지정확도 만을 사용하고 있어, 사용자 입장에서 그 적용성을 평가하는 데에는 한계가 있다. 본 연구의 목적은 초분광영상에 대한 표적탐지 알고리즘의 적용성을 체계적으로 분석하는 것이다. 이를 위하여 표적, 배경, 영상의 분광적 또는 복사적 특성에 관련된 5가지 기준 인자들을 정의하였고, 각 인자의 변이에 따른 6가지 기존 표적탐지 알고리즘의 탐지정확도 변화를 비교하였다. 이와 더불어 영상 크기에 따른 각 알고리즘의 처리시간을 비교하였다. 그 결과 탐지정확도 측면에서는 기준인자에 따라 적용성이 높은 알고리즘의 종류가 다르게 나타났다. 처리시간은 2차 통계값 기반 알고리즘이 다른 알고리즘에 비해 매우 크게 나타났다. 탐지정확도와 처리시간을 종합적으로 고려한 결과 사용하는 영상과 표적 그리고 배경의 특성에 따라 적용성이 높은 알고리즘의 종류가 다른 것으로 나타났다. 따라서 초분광영상에 대한 기존 표적탐지 알고리즘의 적용성은 자료의 특성 및 배경과 표적의 공간적 분광적 관계에 따라 다르게 나타나므로, 사용하는 자료의 특성과 목적에 따라 적용하는 표적탐지 알고리즘의 종류가 달라질 필요가 있다.

Keywords

References

  1. 김광은, 2011. 초분광 영상의 endmember 자동 추출을 위한 수정된 Iterative N-FINDR 기법 개발, 대한원격탐사학회지, 27(5):565-572. https://doi.org/10.7780/kjrs.2011.27.5.565
  2. 김선화, 이규성, 마정림, 국민정, 2005. 초분광 원격탐사의 특성, 처리기법 및 활용 현황, 대한원격탐사학회지, 21(4): 341-369.
  3. 신정일, 김선화, 윤정숙, 김태근, 이규성, 2006. 도시지역의 수문학적 토지피복 분류를 위한 초분광영상의 분광혼합분석, 대한원격탐사학회지, 22(6): 565-574. https://doi.org/10.7780/kjrs.2006.22.6.565
  4. Alam, M.S., M.N. Islam, A. Bal, and M.A. Karim, 2008. Hyperspectral target detection using Gaussian filter and post-processing, Optics and Lasers in Engineering, 46(11): 817-822. https://doi.org/10.1016/j.optlaseng.2008.05.019
  5. Andrew, M.E. and S.L. Ustin, 2008. The role of environmental context in mapping invasive plants with hyperspectral image data, Remote Sensing of Environment, 112(12): 4301-4317. https://doi.org/10.1016/j.rse.2008.07.016
  6. Bell, J.H., B.B. Bowen, and B.A. Martini, 2010. Imaging spectroscopy of jarosite cement in the Jurassic Navajo Sandstone, Remote Sensing of Environment, 114(10): 2259-2270. https://doi.org/10.1016/j.rse.2010.05.002
  7. Bubner, T.P., S.K. Kempinger, and V.K. Shettigara, 2001. An investigation of target detection ability using spectral signatures at hyperspectral resolution, Technical report (DSTO-TR-0807), Defence Science & Technology Organisation, Department of Defence, Australia. (http:// 203.10.217.104/ publications/scientific_record.php?record=4045)
  8. Chang, C.I., 2003. Hyperspectral imaging: Techniques for spectral detection and classification, Kluwer Academic/Plenum Publishers, New York.
  9. Clark, R.N., G.A. Swayze, K.E. Livo, R.F. Kokaly, S.J. Sutley, J.B. Dalton, R.R. McDougal, and C.A. Gent, 2003. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems, Journal of Geophysical Research, 108(5131): 5-1-5-44. (http://speclab.cr.usgs.gov/PAPERS/tetracorder)
  10. Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27(8): 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  11. Goetz, A.F.H., 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view, Remote Sensing of Environment, 113(S1): S5-S16. https://doi.org/10.1016/j.rse.2007.12.014
  12. Goetz, A.F.H., G. Vane, J. Solomon, and B.N. Rock, 1985. Imaging spectrometry for Earth remote sensing, Science, 228(4704): 1147-1153. https://doi.org/10.1126/science.228.4704.1147
  13. Green, R.O., 2010. HyspIRI VSWIR science measurement baseline. Agenda and presentations on 2010 HyspIRI science workshop. Presented at Pasadena, CA, Aug. 24-26, 2010. (http://hyspiri.jpl.nasa.gov/downloads/public/2010_Workshop/day1/day1_3_Green_pres_HyspIRI_VSWIR_Sci_Meas_Green_100824.pdf)
  14. Guanter, L., K. Segl, and H. Kaufmann, 2009. Simulation of optical remote-sensing scenes with application to the EnMAP hyperspectral mission, IEEE transactionsions on Geoscience and Remote Sensing, 47(7): 2340-2351. https://doi.org/10.1109/TGRS.2008.2011616
  15. Heiden, U., K. Segl, S. Roessner, and H. Kaufmann, 2007. Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data, Remote Sensing of Environment, 111(4): 537-552. https://doi.org/10.1016/j.rse.2007.04.008
  16. Jang, G.S., K.A. Sudduth, S.Y. Hong, N.R. Kitchen, and H.L. Palm, 2006. Relating hyperspectral image bands and vegetation indices to corn and soybean yield, Korean Journal of Remote Sensing, 22(3): 183-197. https://doi.org/10.7780/kjrs.2006.22.3.183
  17. Jensen, J.R., 2005. Introductory digital image processing: a remote sensing perspective, 3rd edition, Upper Saddle River, NJ: Pearson Prentice Hall.
  18. Jia, X. and J.A. Richards, 1993. Binary coding of imaging spectrometer data for fast spectral matching and classification, Remote Sensing of Environment, 43(1): 47-53. https://doi.org/10.1016/0034-4257(93)90063-4
  19. Karaska, M.A., R.L. Huguenin, J.L. Beacham, M.H. Wang, J.R. Jensen, and R.S. Kaufmann, 2004. AVIRIS measurements of chlorophyll, suspended minerals, dissolved organic carbon, and turbidity in the Neuse river, North Carolina, Photogrammetric Engineering & Remote Sensing, 70(1): 125-133. https://doi.org/10.14358/PERS.70.1.125
  20. Keshava, N., 2004. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries, IEEE transactions on Geoscience and Remote Sensing, 42(7): 1552-1565. https://doi.org/10.1109/TGRS.2004.830549
  21. Kim, K.E., 2006. A fast algorithm for target detection in high spatial resolution imagery, Korean Journal of Remote Sensing, 22(1): 41-47. https://doi.org/10.7780/kjrs.2006.22.1.41
  22. Lee, K.S., S.H. Kim, J.R. Ma, M.J. Kook, J.I. Shin, Y.D. Eo, and Y.W. Lee, 2006. Spectral characteristics of dry-vegetation cover types observed by hyperspectral data, Korean Journal of Remote Sensing, 22(3): 175-182. https://doi.org/10.7780/kjrs.2006.22.3.175
  23. Manolakis, D. and G. Shaw, 2002. Detection algorithms for hyperspectral imaging applications, IEEE Signal Processing Magazine, January 2002, 29-43.
  24. Manolakis, D., D. Marden, and G.A. Shaw, 2003. Hyperspectral image processing for automatic target detection applications, Lincoln Laboratory Journal, 14(1): 79-116.
  25. Plaza, A., J.A. Benediktsson, J.W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J.C. Tilton, and G. Trianni, 2009. Recent advances in techniques for hyperspectral image processing, Remote Sensing of Environment, 113(S1): S110-S122. https://doi.org/10.1016/j.rse.2007.07.028
  26. Schaepman, M.E., S.L. Ustin, A.J. Plaza, T. Painter, J. Verrelst, and S. Liang, 2009. Earth system science related imaging spectroscopy- An assessment, Remote Sensing of Environment, 113(S1): S123-137. https://doi.org/10.1016/j.rse.2009.03.001
  27. Yoon, Y. and Y. Kim, 2007. Application of Hyperion hyperspectral remote sensing data for wildfire fuel mapping, Korean Journal of Remote Sensing, 23(1): 21-32. https://doi.org/10.7780/kjrs.2007.23.1.21

Cited by

  1. Applicability Evaluation of Endmember Extraction Algorithms on the AISA Hyperspectral Images vol.29, pp.5, 2013, https://doi.org/10.7780/kjrs.2013.29.5.8
  2. Study on Improving Hyperspectral Target Detection by Target Signal Exclusion in Matched Filtering vol.31, pp.5, 2015, https://doi.org/10.7780/kjrs.2015.31.5.7
  3. Iterative Error Analysis 기반 분광혼합분석에 의한 초분광 영상의 표적물질 탐지 기법 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.1.8