Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

COSMO-SkyMed 2 Image Color Mapping Using Random Forest Regression

  • Seo, Dae Kyo (Dept. of Smart ICT Convergence, Konkuk University) ;
  • Kim, Yong Hyun (Dept. of Civil and Environmental Engineering, Seoul National University) ;
  • Eo, Yang Dam (Dept. of Advanced Technology Fusion, Konkuk University) ;
  • Park, Wan Yong (Agency for Defense Development)
  • Received : 2017.08.07
  • Accepted : 2017.08.29
  • Published : 2017.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

SAR (Synthetic aperture radar) images are less affected by the weather compared to optical images and can be obtained at any time of the day. Therefore, SAR images are being actively utilized for military applications and natural disasters. However, because SAR data are in grayscale, it is difficult to perform visual analysis and to decipher details. In this study, we propose a color mapping method using RF (random forest) regression for enhancing the visual decipherability of SAR images. COSMO-SkyMed 2 and WorldView-3 images were obtained for the same area and RF regression was used to establish color configurations for performing color mapping. The results were compared with image fusion, a traditional color mapping method. The UIQI (universal image quality index), the SSIM (structural similarity) index, and CC (correlation coefficients) were used to evaluate the image quality. The color-mapped image based on the RF regression had a significantly higher quality than the images derived from the other methods. From the experimental result, the use of color mapping based on the RF regression for SAR images was confirmed.

Keywords

References

  1. Al-Najja, Y. and Chen, S.D. (2012), Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI, International Journal of Scientific & Engineering Research, Vol. 3, No. 8, pp. 1-5.
  2. Al-Wassai, F.A., Kalyankar, N.V., and Al-Zaky, A.A. (2011), The statistical methods of pixel-based image fusion, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 1, No. 3, pp. 1-10.
  3. Amarsaikhan, D., Blotevogel, H.H., van Genderen, J.L., Ganzorig, M., Gantuya, R., and Nergui, B. (2010), Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification, International Journal of Image and Data Fusion, Vol. 1, No. 1, pp. 83-87. https://doi.org/10.1080/19479830903562041
  4. Breiman, L. (2001), Random forests, Machine Learning, Vol. 45, No. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  5. Caselles, V. and Lopez Garrcia, M.J. (1989), An alternative simple approach to estimate atmospheric correction in multitemporal studies, International Journal of Remote Sensing, Vol. 10, No. 6, pp. 1127-1134. https://doi.org/10.1080/01431168908903951
  6. Chandrakanth, R., Saibaba, J., Varadan, J., and Raj, P.A. (2011), Feasibility of high resolution SAR and multispectral data fusion, 2011 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 24-29 July. Vancouver, BC, Canada, Vol. 1, pp. 356-359
  7. Deng, Q., Chen, Y., Zhang, W., and Yang, J. (2008), Colorization for polarimetric SAR image based on scattering mechanisms, 2008 Congress on Image and Signal Processing, IEEE, 27-30 May, Sanya, Hainan, China, Vol. 5, pp. 697-701.
  8. Du, Y., Teillet, P., and Cihlar, J. (2002), Radiometric normalization of multitemporal high-resolution satellite image with quality control for land cover change detection, Remote Sensing of Environment, Vol. 82, No. 1, pp. 123- 134. https://doi.org/10.1016/S0034-4257(02)00029-9
  9. Gromping, U. (2009), Variable importance assessment in regression: linear regression versus random forest, The American Statistician, Vol. 63, No. 4, pp. 308-319. https://doi.org/10.1198/tast.2009.08199
  10. Hong, G., Zhang, Y., and Mercer, B. (2009), A wavelet and HIS integration method to fuse high resolution SAR with moderate resolution multispectral images, Photogrammetric Engineering & Remote Sensing, Vol. 75, No. 10, pp. 1213-1223. https://doi.org/10.14358/PERS.75.10.1213
  11. Ozdarici, A. and Akyurek, Z. (2010), A comparison of SAR filtering technique on agricultural area identification, ASPRS 2010 Annual Conference, ASPRS, 26-30 April, San Diego, California, unpaginated CD-ROM.
  12. Prasad, A.M., Iverson, L.R., and Liaw, A. (2006), Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, Vol. 9, No. 2, pp. 181-199. https://doi.org/10.1007/s10021-005-0054-1
  13. Rodriquez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. (2015), Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machine, Ore Geology Reviews, Vol. 71, No. 1, pp. 804-818. https://doi.org/10.1016/j.oregeorev.2015.01.001
  14. Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R., and Khovanova, N. (2017), Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation, Biomedical Signal Processing and Control, https://doi.org/10.1016/j.bspc.2017.01.012, (last date accessed: 05 August 2017).
  15. Srimani, P.K. and Prasad, N. (2014), Analysis and comparative study of image fusion techniques for land use and land cover classification on Anthrasanthe Hobli, Karnataka, International Journal of Engineering Research & Technology, Vol.3, No. 6, pp. 1703-1711.
  16. Strobl, S., Boulesteix, A., Kneib, T., Augustin, T., and Zeileis, A. (2008), Conditional variable importance for random forests, BMC Bioinformatics, Vol. 9, No. 307, pp. 1-11. https://doi.org/10.1186/1471-2105-9-1
  17. Uhlmann, S. and Kiranyaz, S. (2014), Integrating color features in polarmetric SAR image classification, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 4, pp. 2197-2216. https://doi.org/10.1109/TGRS.2013.2258675
  18. Wang, X.L. and Chen, C.X. (2016), Image fusion for synthetic aperture radar and multispectral images based on subband-modulated non-subsampled contourlet transform and pulse coupled neural network methods, The Imaging Science Journal, Vol. 64, No. 2, pp. 87-93. https://doi.org/10.1080/13682199.2015.1136101
  19. Wang, X., Ge, L., and Li, X. (2012), Evaluation of filters for ENVISAT ASAR speckle suppression in pasture area, ISPRS Annuals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS, 25 August - 01 September, Melbourne, Australia, Vol. l-7, pp. 341-346.
  20. Wang, Z. and Bovik, A.C. (2002), A universal image quality index, IEEE Signal Processing Letters, Vol. 9, No. 3, pp. 81-84. https://doi.org/10.1109/97.995823
  21. Wang, Z., Bovik, A.C., Sheikh, H.R., and Simoncelli, E.P. (2004), Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612. https://doi.org/10.1109/TIP.2003.819861