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Pan-sharpening Effect in Spatial Feature Extraction

  • Received : 2011.05.02
  • Accepted : 2011.05.30
  • Published : 2011.06.30

Abstract

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

Keywords

Acknowledgement

Supported by : NRF

References

  1. Bethune, S., Muller, F., Donnay, J. P., 1998. Fusion of multispectral and panchromatic images by local mean and variance matching filtering techniques, Fusion of Earth Data, Sophia Antipolis, France, pp.28-30.
  2. Canny, J., 1986. A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6): 679-698.
  3. Choi, M. J., J. H. Song, D. C. Seo, D. H. Lee, and H. S. Lim, 2007. Introduction of a fast substitute wavelet intensity method to pan-sharpening technique, Korean Journal of Remote Sensing, 23(5): 347-353. https://doi.org/10.7780/kjrs.2007.23.5.347
  4. Colditz, R. R., T. Wehrmann, M. Bachmann, K. Steinnocher, M. Schmidt, G. Strunz, and S. Dech, 2006. Influence of Image Fusion Approaches on Classification Accuracy - A Case Study, International Journal of Remote Sensing, 27(15): 3311-3335. https://doi.org/10.1080/01431160600649254
  5. Crippen, R. E., 1989. A Simple Spatial Filtering Routine for the Cosmetic Removal of Scan- Line Noise from Landsat TM P-Tape Imagery, Photogrammetric Engineering & Remote Sensing, 55 (3): 327-331.
  6. Gangkofner, U. G., P. S. Pradhan, and D. W. Holcomb, 2008. Optimizing the High-pass Filter Addition Technique for Image Fusion, Photogrammetric Engineering and Remote Sensing, 74(9): 1107-1118. https://doi.org/10.14358/PERS.74.9.1107
  7. Geosage, http://www.geosage.com/highview/ imagefusion.html.
  8. Han, D. Y., H. J. Kim, and Y. I. Kim, 2007. Automatic Registration of QuickBird Image and Digital Map, (in Korean). Proc. Korea Society of Remote Sensing Spring Conference, Taejeon.
  9. Hong, G. and Y. Zhang, 2008. Comparison and improvement of wavelet-based image fusion, International Journal of Remote Sensing, 29(3): 673-691. https://doi.org/10.1080/01431160701313826
  10. Hong, G., Y. Zhang, A. Zhang, F. Zhou, and J. Li, 2007. Fusion of MODIS and Radarsat data for crop type classification - an initial study, ISPRS Workshop on Updating Geo-spatial Databases with Imagery & the 5th ISPRS Workshop on DMGISs, August 28-29, 2007, Urumchi, Xinjiang, China.
  11. Kalpoma, K. A., and J. I. Kudoh, 2007. Image Fusion Processing for IKONOS 1-m Color Imagery, IEEE Transaction on Geoscience and Remote Sensing, 45(10): 3075-3086. https://doi.org/10.1109/TGRS.2007.897692
  12. Kim, S. H., S. J. Kang, and K. S. Lee, 2010. Comparison of fusion methods for generating 250m MODIS image, Korean Journal of Remote Sensing, 26(3): 305-316. https://doi.org/10.7780/kjrs.2010.26.3.305
  13. Lowe, D. G., 2004. Distinctive image features from scale-invariant key points, Int. J. Comput. Vis., 60(2): 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  14. Mikolajczyk, K. and C. Schmid, 2005. A performance evaluation of local descriptors, IEEE T. Pattern Anal., 27(10): 1615-30. https://doi.org/10.1109/TPAMI.2005.188
  15. Munechika, C. K., J. S. Warnick, C. Salvaggio, and J. R. Schott, 1993. Resolution enhancement of multispectral image data to improve classification accuracy, Photogrammetric Engineering and Remote Sensing, 59(1): 67-72.
  16. Pohl, C., and J. L. van Genderen, 1998. Multisensor image fusion in remote sensing: concepts, methods and applications, International Journal of Remote Sensing, 19(5): 823-854. https://doi.org/10.1080/014311698215748
  17. Prinz, B., R. Wiemker, and H. Spitzer, 1997. Simulation of high resolution satellite imagery form multispectral airborne scanner imagery for accuracy assessment of fusion algorithms, Proceedings of the ISPRS Joint Workshop "Sensors and Mapping form space" of working group I/1, I/3 and IV/4, Hannover, Germany, October 1997.
  18. Renza, D., E. Martinez, and A. Arquero, 2009. Optimizing Classification Accuracy of Remotely Sensed Imagery with DT-CWT Fused Images, Lecture Notes In Computer Science; Vol. 5856, Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp.1031-1038.
  19. Shaban, M. A. and O. Dikshit, 2002. Evaluation of the merging of SPOT multispectral and panchromatic data for classification of an urban environment, International Journal of Remote Sensing, 23(2): 249-262. https://doi.org/10.1080/01431160010007088
  20. Shi, W., C. O. Zhu, Y. Tian, and J. Nichol, 2005. Wavelet-based image fusion and quality assessment, International Journal of Applied Earth Observation and Geoinformation, 6: 241-251. https://doi.org/10.1016/j.jag.2004.10.010
  21. Teggi, S., R. Cecchi, and F. Serafini, 2003. TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a trous' algorithm, International Journal of Remote Sensing, 24(6): 1287-1301. https://doi.org/10.1080/01431160210144561
  22. Vrabel, J., 1996. Multispectral Imagery Band Sharpening Study, Photogrammetric Engineering & Remote Sensing, 62(9): 1075-1083.
  23. Wald, L., 2002. Data Fusion: Definitions and Architectures-Fusion of Images of different Spatial Resolutions.
  24. Wang, Z., 2002. A Universal Image Quality Index, IEEE Signal Processing Letters, 9(3): 1-4. https://doi.org/10.1109/97.988714
  25. Zhang, Y., 2004. Highlight Article: Understanding Image Fusion, Photogrammetric Engineering & Remote Sensing, 70(6): 657-661.
  26. Zhang, Y. and G. Hong, 2005. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images, Information Fusion, 6: 225-234. https://doi.org/10.1016/j.inffus.2004.06.009
  27. Zhou, J., D. L. Civco, and J. A. Silander, 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data, International Journal of Remote Sensing, 19(4): 743-757. https://doi.org/10.1080/014311698215973