Browse > Article
http://dx.doi.org/10.7848/ksgpc.2017.35.4.319

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)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.35, no.4, 2017 , pp. 319-326 More about this Journal
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
Random Forest Regression; Color Mapping; Synthetic Aperture Radar; COSMO-SkyMed 2; WorldView-3;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.
2 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.   DOI
3 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.   DOI
4 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).   DOI
5 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.
6 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.   DOI
7 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.   DOI
8 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.
9 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.
10 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.   DOI
11 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.
12 Wang, Z. and Bovik, A.C. (2002), A universal image quality index, IEEE Signal Processing Letters, Vol. 9, No. 3, pp. 81-84.   DOI
13 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.   DOI
14 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
15 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.   DOI
16 Breiman, L. (2001), Random forests, Machine Learning, Vol. 45, No. 1, pp. 5-32.   DOI
17 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.   DOI
18 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.
19 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.   DOI
20 Gromping, U. (2009), Variable importance assessment in regression: linear regression versus random forest, The American Statistician, Vol. 63, No. 4, pp. 308-319.   DOI
21 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.   DOI