Korean Journal of Remote Sensing (대한원격탐사학회지)

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

DOI QR Code

Anomaly Detection from Hyperspectral Imagery using Transform-based Feature Selection and Local Spatial Auto-correlation Index

자료 변환 기반 특징 선택과 국소적 자기상관 지수를 이용한 초분광 영상의 이상값 탐지

  • Park, No-Wook (Department of Geoinformatic Engineering, Inha University) ;
  • Yoo, Hee-Young (Geoinformatic Engineering Research Institute, Inha University) ;
  • Shin, Jung-Il (Department of Geoinformatic Engineering, Inha University) ;
  • Lee, Kyu-Sung (Department of Geoinformatic Engineering, Inha University)
  • 박노욱 (인하대학교 지리정보공학과) ;
  • 유희영 (인하대학교 지리정보공학연구소) ;
  • 신정일 (인하대학교 지리정보공학과) ;
  • 이규성 (인하대학교 지리정보공학과)
  • Received : 2012.07.09
  • Accepted : 2012.08.02
  • Published : 2012.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a two-stage methodology for anomaly detection from hyperspectral imagery that consists of transform-based feature extraction and selection, and computation of a local spatial auto-correlation statistic. First, principal component transform and 3D wavelet transform are applied to reduce redundant spectral information from hyperspectral imagery. Then feature selection based on global skewness and the portion of highly skewed sub-areas is followed to find optimal features for anomaly detection. Finally, a local indicator of spatial association (LISA) statistic is computed to account for both spectral and spatial information unlike traditional anomaly detection methodology based only on spectral information. An experiment using airborne CASI imagery is carried out to illustrate the applicability of the proposed anomaly detection methodology. From the experiments, anomaly detection based on the LISA statistic linked with the selection of optimal features outperformed both the traditional RX detector which uses only spectral information, and the case using major principal components with large eigen-values. The combination of low- and high-frequency components by 3D wavelet transform showed the best detection capability, compared with the case using optimal features selected from principal components.

이 논문에서는 초분광 영상으로부터 이상값을 탐지하기 위해 자료 변환 기반 특징 추출과 선정 및 국소적 자기상관지수를 이용하는 2단계 방법론을 제안한다. 초분광 영상이 제공하는 중복된 분광 정보들의 축약을 위해 우선적으로 주성분 변환과 3차원 웨이브렛 변환을 적용하였다. 그리고 축약된 자료 변환 기반 특징을 대상으로 왜도와 국소적 왜도 비율을 함께 고려하여 이상값 탐지를 위한 유효 특징을 선정하였다. 최종적으로 기존 분광 정보만을 이용하는 이상값 탐지 방법론들에 공간 자기상관성을 함께 고려할 수 있도록 국소적 자기상관지수(LISA)를 이상값 탐지 방법론으로 적용하였다. 제안 방법론의 적용성 평가를 위해 항공 CASI 자료를 대상으로 한 실험을 수행하였다. 실험 결과, 기존 분광 정보만을 고려하는 RX detector나 고유값 기반 주요 주성분만을 이용하는 경우에 비해 유효 특징 선정과 연계된 LISA 통계값이 높은 탐지 능력을 나타내었다. 또한 3차원 웨이브렛 변환 기반 저주파와 고주파 특징들을 결합한 경우가 유효 주성분을 사용하는 경우에 비해 가장 높은 탐지 성능을 나타냈다.

Keywords

References

  1. 김대성, 김형태, 2008. 누적 유사도 측정을 이용한 자동 임계값 결정 기법 -다중분광 및 초분광영상의무감독 변화탐지를 목적으로, 대한원격탐사학회지, 24(4):341-349.
  2. 김선화, 이규성, 마정림, 국민정, 2005. 초분광 원격탐사의 특성, 처리기법 및 활용 현황, 대한원격탐사학회지, 21(4):341-369.
  3. 신정일, 2012. 초분광영상에 대한 지표물 탐지 알고리즘의 적용성 비교 및 성능 개선, 인하대학교 박사학위 논문.
  4. 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
  5. Anselin, L., 1995. Local indicators of spatial association-LISA, Geographical Analysis, 27(2): 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  6. Banerjee, A., P. Burlina, and C. Diehl, 2006. A support vector method for anomaly detection in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2282-2291. https://doi.org/10.1109/TGRS.2006.873019
  7. Bruce, L.M., C.H. Koger, and J. Li, 2002. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction, IEEE Transactions on Geoscience and Remote sensing, 40(10): 2331-2338. https://doi.org/10.1109/TGRS.2002.804721
  8. Camps-Valls, G. and L. Bruzzone, 2005. Kernelbased methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1351-1362. https://doi.org/10.1109/TGRS.2005.846154
  9. Chang, C.-I., 2007. Hyperspectral Data Exploitation: Theory and Applications, Wiley.
  10. Chang, C.-I. and S.-S. Chiang, 2002. Anomaly detection and classification for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 40(6): 1314-1325. https://doi.org/10.1109/TGRS.2002.800280
  11. Chen, Z. and R. Ning, 2004. Breast volume denoising and noise characterization by 3D wavelet transform, Computerized Medical Imaging and Graphics, 28(5): 235-246. https://doi.org/10.1016/j.compmedimag.2004.04.004
  12. Cheung, N., C. Tang, A. Ortega, and C.S. Raghavendra, 2006. Efficient wavelet-based predictive Slepian-Wolf coding for hyperspectral imagery, Signal Processing, 86(11): 3180-3195. https://doi.org/10.1016/j.sigpro.2006.03.016
  13. Cochrane, M.A., 2000. Using vegetation reflectance variability for species level classification of hyperspectral data, International Journal of Remote Sensing, 21(10): 2075-2087. https://doi.org/10.1080/01431160050021303
  14. Davis, J.C., 1986. Statistics and Data Analysis in Geology. Wiley.
  15. Deutsch, C.V. and A.G. Journel, 1998. GSLIB: Geostatistical Software Library and User's Guide, 2nd Edition, Oxford University Press.
  16. Ghugre, N.R., M. Martin, M. Scadeng, S. Ruffins, T. Hiltner, R. Pautler, C. Waters, C. Readhead, R. Jacobs, and J.C. Wood, 2003. Superiority of 3D wavelet-packet denoising in MR microscopy, Magnetic Resonance Imaging, 21(8): 913-921. https://doi.org/10.1016/S0730-725X(03)00191-7
  17. Goovaerts, P., G.M. Jacquez, and A. Marcus, 2005. Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of anomalies, Remote Sensing of Environment, 95(3): 351-367. https://doi.org/10.1016/j.rse.2004.12.021
  18. Gu, Y., Y. Liu, and Y. Zhangm, 2008. A selective KPCA algorithm based on high-order statistics for anomaly detection in hyperspectral imagery, IEEE Geoscience and Remote Sensing Letters, 5(1): 43-47. https://doi.org/10.1109/LGRS.2007.907304
  19. Igamberdiev, R.M., G. Renzdoerffer, R. Bill, H. Schubert, M. Bachmann, and B. Lennartz, 2011. Determination of chlorophyll content of small water bodies (kettle holes) using hyperspectral airborne data, International Journal of Applied Earth Observation and Geoinformation, 13(6): 912-921. https://doi.org/10.1016/j.jag.2011.04.001
  20. Kurz, T.H., J. Dewit, S.J. Buckley, J.B. Thurmond, and D.W. Hunt, 2012. Hyperspectral image analysis of different carbonate lithologies (limestone, karst and hydrothermal dolomites): the Pozalagua Quarry case study (Cantabria, North-west Spain), Sedimentology, 59(2): 623-645. https://doi.org/10.1111/j.1365-3091.2011.01269.x
  21. Kwon, H. and N.M. Nasrabadi, 2005. Kernel RXalgorithm: a nonlinear anomaly detector for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 43(2): 388-397. https://doi.org/10.1109/TGRS.2004.841487
  22. Lillesand, T.M., R.W. Kiefer, and J.W. Chipman, 2008. Remote Sensing and Image Interpretation, 6th Edition, Wiley.
  23. Luo, L., F. Wu, S. Li, Z. Xiong, and Z. Zhuang, 2004. Advanced motion threading for 3D wavelet video coding, Signal Processing: Image Communication, 19(7): 601-616. https://doi.org/10.1016/j.image.2004.05.004
  24. Manolakis, D. and G. Shaw, 2002. Detection algorithms for hyperspectral imaging applications, IEEE Signal Processing Magazine, 19(1):29-43. https://doi.org/10.1109/79.974724
  25. Melgani, F. and L. Bruzzone, 2004. Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1778-1790. https://doi.org/10.1109/TGRS.2004.831865
  26. Nunez, J., X. Otazu, O. Fors, A. Prades, V. Pala, and R. Arboiol, 1999. Multiresolution-based image fusion with additive wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1204-1211. https://doi.org/10.1109/36.763274
  27. Otazu, X., M. Gonzales-Audicana, O. Fors, and J. Nunez, 2005. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods, IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2376- 2385. https://doi.org/10.1109/TGRS.2005.856106
  28. Plaza, A., P. Martínez, J. Plaza, and R. Pérez, 2005. Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations, IEEE Transactions on Geoscience and Remote Sensing, 43(3): 466-479. https://doi.org/10.1109/TGRS.2004.841417
  29. Reed, I.S. and X. Yu, 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustic, Speech and Signal Processing, 38(10): 1760-1770. https://doi.org/10.1109/29.60107
  30. Roessner, S., K. Segl, U. Heiden, and H. Kaufmann, 2001. Automated differentiation of urban surfaces based on airborne hyperspectral imagery, IEEE Transactions on Geoscience and Remote sensing, 39(7): 1525-1532. https://doi.org/10.1109/36.934082
  31. Stein, D., S. Beaven, L. Hoff, E. Winter, A. Schaum, and A. Stocker, 2002. Anomaly detection from hyperspectral imagery, IEEE Signal Processing Magazine, 19(1): 58-69. https://doi.org/10.1109/79.974730
  32. Swets, J.A., 1988. Measuring the accuracy of diagnostic systems, Science, 240(4857): 1285- 1293. https://doi.org/10.1126/science.3287615
  33. Yoo, H.Y., K. Lee, and B.D. Kwon, 2007. Application of the 3D discrete wavelet transformation scheme to remotely sensed image classification, Korean Journal of Remote Sensing, 23(5): 355- 363. https://doi.org/10.7780/kjrs.2007.23.5.355
  34. Yoo, H.Y., K. Lee, and B.D. Kwon, 2009. Quantitative indices based on 3D discrete wavelet transform for urban complexity estimation using remotely sensed imagery, International Journal of Remote Sensing, 30(23): 6219-6239. https://doi.org/10.1080/01431160902842359

Cited by

  1. The Impacts of Decomposition Levels in Wavelet Transform on Anomaly Detection from Hyperspectral Imagery vol.28, pp.6, 2012, https://doi.org/10.7780/kjrs.2012.28.6.3