DOI QR코드

DOI QR Code

The Applicability for Earth Surface Monitoring Based on 3D Wavelet Transform Using the Multi-temporal Satellite Imagery

다중시기 위성영상을 이용한 3차원 웨이블릿 변환의 지구모니터링 응용가능성 연구

  • Yoo, Hee-Young (Department of Geography, University of Colorado at Boulder) ;
  • Lee, Ki-Won (Department of Information System Engineering, Hansung University)
  • 류희영 (콜로라도대학교 볼더캠퍼스 지리학과) ;
  • 이기원 (한성대학교 정보시스템공학과)
  • Received : 2011.07.03
  • Accepted : 2011.08.08
  • Published : 2011.10.31

Abstract

Satellite images that have been obtained periodically and continuously are very effective data to monitor the changes of Earth's surface. Traditionally, the studies on change detection using satellite images have mainly focused on comparison between two results after analyzing two images respectively. However, the interests in researches to catch smooth trends and short duration events from continual multi-temporal images have been increased recently. In this study, we introduce and test an approach based on 3D wavelet transform to analyze the multi-temporal satellite images. 3D wavelet transform can reduce the dimensions of data conserving main trends. Also, it is possible to extract important patterns and to analyze spatial and temporal relations with neighboring pixels using 3D wavelet transform. As a result, 3D wavelet transform is useful to capture the long term trends and short-term events rapidly. In addition, we can expect to get new information through sub-bands of 3D wavelet transform which provide different information by decomposed direction.

주기적이고 지속적으로 자료를 얻을 수 있는 위성영상은 지표면의 변화를 모니터링 하기 위한 매우 효과적인 자료이다. 위성영상을 이용한 기존의 변화탐지 연구는 두 시점의 지표 특성을 각각 분석해 서로 비교하여 변화를 밝혀내는 연구를 주로 해왔다. 그러나 최근에는 연속성을 갖는 다중 시기 위성영상으로부터 전체적인 경향이나 단기적인 변화를 찾아내는 연구에 관심이 높아지고 있다. 이 연구에서는 다중 시기 위성영상을 분석하기 위해 3차원 웨이블릿 변환 기반의 기법을 제안하고 테스트해보았다. 3차원 웨이블릿 변환을 이용하면 자료의 중요한 특성은 보존하면서 차원을 줄이는 것이 가능하다. 또한 다중 시기의 자료로부터 주요 패턴을 간추려 내고 공간, 시간적으로 인접한 주변 화소와의 관계를 파악할 수 있다. 연구 결과, 3차원 웨이블릿 변환 기법은 전체적인 경향성이나 특별한 변화 특성을 빠른 시간내에 밝혀내는 데 유용할 뿐만 아니라 분해 방향에 따라 각기 다른 정보를 제공해 주는 하위 밴드를 통해 새로운 정보를 얻을 수 있을 것으로 기대된다.

Keywords

References

  1. Acharyya, M., De, R.K., and Kundu, M.K., 2003, Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework. IEEE Transactions on Geoscience and Remote Sensing, 41, 2900-2905. https://doi.org/10.1109/TGRS.2003.815398
  2. Achim, A., Tsakalides, P., and Bezerianos, A., 2003, SAR image denoising via bayesian wavelet shrinkage based on heavy-tailed modeling. IEEE Transaction on Geoscience and Remote Sensing, 41, 1773-1784. https://doi.org/10.1109/TGRS.2003.813488
  3. Argenti, F. and Alparone, L., 2002, Speckle removal from SAR images in the undecimated wavelet domain. IEEE Transactions on Geoscience and Remote Sensing, 40, 2363-2374. https://doi.org/10.1109/TGRS.2002.805083
  4. Boucheron, L.E. and Creusere, C.D., 2005, Lossless wavelet- based compression of digital elevation maps for fast and efficient search and retrieval. IEEE Transactions on Geoscience and Remote Sensing, 43, 1210-1214. https://doi.org/10.1109/TGRS.2004.841477
  5. Bruce, L.M., Koger, C.H., and Li, J., 2002, Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 40, 2331-2338. https://doi.org/10.1109/TGRS.2002.804721
  6. Chen, Z. and Ning, R., 2004, Breast volume denoising and noise characterization by 3D wavelet transform. Computerized Medical Imaging and Graphics, 28, 235-246. https://doi.org/10.1016/j.compmedimag.2004.04.004
  7. Chen, Z., Zhao, Z., Gong, P., and Zeng, B., 2006, A new process for the segmentation of high resolution remote sensing imagery. International Journal of Remote Sensing, 27, 4991-5001. https://doi.org/10.1080/01431160600658131
  8. Cohen, W.B., Spies, T., Alig, R.J., Oetter, D.R., Maiersperger, T.K., and Fiorella, M., 2002, Characterizing 23 years (1972-95) of stand replacement disturbance in western Oregon forests with Landsat imagery. Ecosystems, 5, 122-137. https://doi.org/10.1007/s10021-001-0060-X
  9. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., and Lambin, E., 2004, Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25, 1565-1596. https://doi.org/10.1080/0143116031000101675
  10. Fry, J.A., Coan, M.J., Homer, C.G., Meyer, D.K., and Wickham, J.D., 2009, Completion of the National Land Cover Database (NLCD) 1992-2001 Land Cover Change Retrofit product. U.S. Geological Survey Open- File Report, 2008-1379, 18 p.
  11. Fung, T., 1990, An assessment of TM imagery for land cover change detection. IEEE Transactions on Geoscience and Remote Sensing, 28, 681-684. https://doi.org/10.1109/TGRS.1990.572980
  12. Garcia-Haro, F.J., Gilabert, M.A., and Melia, J., 2001, Monitoring fire-affected areas using Thematic Mapper data. International Journal of Remote Sensing, 22, 533- 549. https://doi.org/10.1080/01431160050505847
  13. Germain, M., Bénié, G.B., Boucher, J., Foucher, S., Fung, K., and Goïta, K., 2003, Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 41, 1765-1771. https://doi.org/10.1109/TGRS.2003.811695
  14. Ghugre, N.R., Martin, M., Scadeng, M., Ruffins, S., Hiltner, T., Pautler, R., Waters, C., Readhead, C., Jacobs, R., and Wood, J.C., 2003, Superiority of 3D waveletpacket denoising in MR microscopy. Magnetic Resonance Imaging, 21, 913-921. https://doi.org/10.1016/S0730-725X(03)00191-7
  15. González-Audícana, M., Saleta, J.L., Catalán, R.G., and García, R., 2004, Fusion of multispectral and panchromatic Images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 42, 1291-1299. https://doi.org/10.1109/TGRS.2004.825593
  16. Goodwin, N.R., Coops, N.C., Wulder, M.A., and Gillanders, S., 2008, Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 12, 3680-3689.
  17. Healey, S.P., Yang, Z., Cohen, W.B., and Pierce, D.J., 2006, Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sensing of Environment, 101, 115-126. https://doi.org/10.1016/j.rse.2005.12.006
  18. Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., Van Driel, J.N., and Wickham, J., 2007, Completion of the 2001 National Land Cover Database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 73, 337-341.
  19. Hostert, P., Roder, A., and Hill, J., 2003, Coupling spectral unmixing and trend analysis for monitoring of longterm vegetation dynamics in Mediterranean rangelands. Remote Sensing of Environment, 87, 183-197. https://doi.org/10.1016/S0034-4257(03)00145-7
  20. Kaewpijit, S., Moigne, J.L., and Ghazawi, T., 2003, Automatic reduction of hyperspectral imagery using wavelet spectral analysis. IEEE Transactions on Geoscience and Remote Sensing, 41, 863-871. https://doi.org/10.1109/TGRS.2003.810712
  21. Kennedy, R.E., Yang, Z., and Cohen, W.B., 2010, Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment, 114, 2897-2910. https://doi.org/10.1016/j.rse.2010.07.008
  22. Klock, H., Polzer, A., and Buhmann, J M., 1997, Regionbased motion compensated 3D-wavelet transform coding of video. 1997 IEEE Conference on Image Processing, Santa Barbara, 2, 776-779.
  23. Koger, C.H., Bruce, L.M., Shawa, D.R., and Reddyc, K.N., 2003, Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max). Remote Sensing of Environment, 86, 108-119. https://doi.org/10.1016/S0034-4257(03)00071-3
  24. Lawrence, R. and Ripple, W.J., 1999, Calculating change curves for multi-temporal satellite imagery: Mount St. Helens 1980-1995. Remote Sensing of Environment, 67, 309-319. https://doi.org/10.1016/S0034-4257(98)00092-3
  25. Lu, D., Mausel, P., Brondizio, E., and Moran, E., 2004, Change detection techniques. International Journal of Remote Sensing, 25, 2365-2407. https://doi.org/10.1080/0143116031000139863
  26. Luo, L., Wu, F., Li, S., Xiong, Z., and Zhuang, Z., 2004, Advanced motion threading for 3D wavelet video coding. Signal Processing: Image Communication, 19, 601- 616. https://doi.org/10.1016/j.image.2004.05.004
  27. Niedermeier, A., Romaneeben, E., and Lehner, S., 2000, Detection of coastlines in SAR images using wavelet methods. IEEE Transactions on Geoscience and Remote Sensing, 8, 2270-2281.
  28. Olsson, H., 2009, A method for using Landsat time series for monitoring young plantations in boreal forests. International Journal of Remote Sensing, 30, 5117-5131. https://doi.org/10.1080/01431160903022993
  29. Roder, A., Udelhoven, T., Hill, J., del Barrio, G., and Tsiourlis, G., 2008, Trend analysis of Landsat-TM and ETM+imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece. Remote Sensing of Environment, 112, 2863-2875. https://doi.org/10.1016/j.rse.2008.01.018
  30. Tso, B. and Olsen, R.C., 2005, A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process. Remote Sensing of Environment, 97, 127-136. https://doi.org/10.1016/j.rse.2005.04.021
  31. Tucker, C.J., 1979, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  32. Viedma, O., Meliá, J., Segarra, D., and García-Haro, J., 1997, Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing of Environment, 61, 383-398. https://doi.org/10.1016/S0034-4257(97)00048-5
  33. Vogelmann, J.E., Sohl, T., and Howard, S.M., 1998, Regional characterization of land cover using multiple sources of data. Photogrammetric Engineering and Remote Sensing, 64, 45-57.
  34. Xian, G., Homer, C., and Fry, J., 2009, Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment, 113, 1133- 1147. https://doi.org/10.1016/j.rse.2009.02.004
  35. Xiong, R., Xu, J., Wu, F., and Li, S., 2005, Studies on spatial scalable frameworks for motion aligned 3D wavelet video coding, Proceedings of Society of Photographic Instrumentation Engineers, 5960, Bellingham.
  36. Yoo, H.Y., Lee, K., and Kwon, B.D., 2007, Application of the 3D discrete wavelet transformation scheme to remotely sensed image classification. Korean Journal of Remote Sensing, 23, 355-363. https://doi.org/10.7780/kjrs.2007.23.5.355
  37. Yoo, H.Y., Lee, K., and Kwon, B.D., 2009, The quantitative indices based on 3D discrete wavelet transform for urban complexity estimation using remotely sensed imagery. International Journal of Remote Sensing, 30, 6219-6239. https://doi.org/10.1080/01431160902842359
  38. Yoo, J.M. and Yoo, H., 2006, Surface Emissivity Derived From Satellite Observations: Drought Index, Journal of Korean Earth Science Society, 27, 787-803.
  39. Yunhao, C., Lei, D., Jing, L., Xiaobing, L., and Peijun, S., 2006, A new wavelet-based image fusion method for remotely sensed data. International Journal of Remote Sensing, 27, 1465-1476. https://doi.org/10.1080/01431160500474365
  40. Zhu, C. and Yang, X., 1998, Study of remote sensing image texture analysis and classification using wavelet. International Journal of Remote Sensing, 19, 3197-3203. https://doi.org/10.1080/014311698214262