(43-1) 15 * << * >> * Русский * English * Содержание * Все выпуски

A parallel fusion method of remote sensing image based on NSCT
Xue X., Xiang F., Wang H.

The School of Computer and Information Engineering, Anyang Normal University, Anyang 455000, Henan, China

 PDF, 1648 kB

DOI: 10.18287/2412-6179-2019-43-1-123-131

Страницы: 123-131.

Аннотация:
Remote sensing image fusion is very important for playing the advantages of a variety of remote sensing data. However, remote sensing image fusion is large in computing capacity and time consuming. In this paper, in order to fuse remote sensing images accurately and quickly, a parallel fusion algorithm of remote sensing image based on NSCT (nonsubsampled contourlet transform) is proposed. In the method, two important kinds of remote sensing image, multispectral image and panchromatic image are used, and the advantages of parallel computing in high performance computing and the advantages of NSCT in information processing are combined. In the method, based on parallel computing, some processes with large amount of calculation including IHS (Intensity, Hue, Saturation) transform, NSCT, inverse NSCT, inverse IHS transform, etc., are done. To realize the method, multispectral image is processed with IHS transform, and the three components, I, H, and S are gotten. The component I and the panchromatic image are decomposed with NSCT. The obtained low frequency components of NSCT are fused with the fusion rule based on the neighborhood energy feature matching, and the obtained high frequency components are fused with the fusion rule based on the subregion variance. Then the low frequency components and the high frequency components after fusion are processed with the inverse NSCT, and the fused component is gotten. Finally, the fused component, the component H and the component S are processed with the inverse IHS transform, and the fusion image is obtained. The experiment results show that the proposed method can get better fusion results and faster computing speed for multispectral image and panchromatic image.

Ключевые слова:
image fusion, remote sensing image, panchromatic image, multispectral image, nonsubsampled contourlet transform, IHS transform, parallel computing.

Цитирование:
Xue, X. A parallel fusion method of remote sensing image based on NSCT / X. Xue, F. Xiang, H. Wang // Computer Optics. - 2019. - Vol. 43, Issue 1. - P. 123-131. - DOI: 10.18287/2412-6179-2019-43-1-123-131.

Литература:
  1. Yang, Y. Multiple visual features measurement with gradient domain guided filtering for multisensor image fusion / Y. Yang, Y. Que, S. Huang, P. Lin // IEEE Transactions on Instrumentation and Measurement. – 2017. – Vol. 66, Issue 4. – P. 691-703. – DOI: 10.1109/TIM.2017.2658098.
  2. Song, H. Spatiotemporal satellite image fusion using deep convolutional neural networks / H. Song, Q. Liu, G. Wang, R. Hang, B. Huang // IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. – 2018. – Vol. 11, Issue 3. – P. 821-829. – DOI: 10.1109/JSTARS.2018.2797894.
  3. Chen, Y. Robust multi-focus image fusion using edge model and multi-matting / Y. Chen, J. Guan, W.-K. Cham // IEEE Transactions on Image Processing. – 2018. – Vol. 27, Issue 3. – P. 1526-1541. – DOI: 10.1109/TIP.2017.2779274.
  4. Luo, X. Image fusion with contextual statistical similarity and nonsubsampled shearlet transform / X. Luo, Z. Zhang, B. Zhang, X. Wu // IEEE Sensors Journal. – 2017. – Vol. 17, Issue 6. – P. 1760-1771. – DOI: 10.1109/JSEN.2016.2646741.
  5. Chien, C.-L. Image fusion with no gamut problem by improved nonlinear HIS transforms for remote sensing / C.-L. Chien, W.-H. Tsai // IEEE Transactions on Geoscience and Remote Sensing. – 2014. – Vol. 52, Issue 1. – P. 651-663. – DOI: 10.1109/TGRS.2013.2243157.
  6. Da Cunha, A.L. The nonsubsampled Contourlet transform: Theory, design, and applications / A.L. Da Cunha, J. Zhou, M.N. Do // IEEE Transactions on Image Processing. – 2006. – Vol. 15, Issue 10. – P. 3089-3101. – DOI: 10.1109/TIP.2006.877507.
  7. Do, M.N. Framing pyramids / M.N. Do, M. Vetterli // IEEE Transactions on Signal Processing. – 2003. – Vol. 51, Issue 9. – P. 2329-2342. – DOI: 10.1109/TSP.2003.815389.
  8. Do, M.N. The Contourlet transform: An efficient directional multiresolution image representation / M.N. Do, M. Vetterli // IEEE Transactions on Image Processing. – 2005. – Vol. 14, Issue 12. – P. 2091-2106. – DOI: 10.1109/TIP.2005.859376.
  9. Bhatnagar, G. Directive contrast based multimodal medical image fusion in NSCT domain / G. Bhatnagar, Q.M.J. Wu, Z. Liu // IEEE Transactions on Multimedia. – 2013. – Vol. 15, Issue 5. – P. 1014-1024. – DOI: 10.1109/TMM.2013.2244870.
  10. da Cunha, A.L. The nonsubsampled Contourlet transform: Theory, design, and applications. IEEE Transactions on Image Processing. – 2006. – Vol. 15, Issue 10. – P. 3089-3101. – DOI: 10.1109/TIP.2006.877507.
  11. Shahdoosti, H.R. Fusion of MS and PAN images preserving spectral quality. IEEE Geoscience and Remote Sensing Letters. – 2015. – Vol. 12, Issue 3. – P. 611-615. – DOI: 10.1109/LGRS.2014.2353135.
  12. Choi, J. A new adaptive component substitution based satellite image fusion by using partial replacement / J. Choi, K. Yu, Y. Kim // IEEE Transactions on Geoscience and Remote Sensing. – 2010. – Vol. 49, Issue 1. – P. 295-309. – DOI: 10.1109/TGRS.2010.2051674.
  13. Ma, J. Wavelet fusion on ratio images for change detection in SAR images / J. Ma, M. Gong, Z. Zhou // IEEE Geosci­ence and Remote Sensing Letters. – 2012. – Vol. 9, Issue 6. – P. 1122-1126. – DOI: 10.1109/LGRS.2012.2191387.
  14. Qingxi, T. Beijing-1 micro-satellite and its data applications / T. Qingxi, W. Zheng // Spacecraft Engineering. – 2007. – Vol. 16, Issue 2. – P. 1-5.
  15. Ban, Y. Object-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land-cover mapping / Y. Ban, A. Jacob // IEEE Transactions on Geoscience and Remote Sensing. – 2013. – Vol. 51, Issue 4. – P. 1998-2006. – DOI: 10.1109/TGRS.2012.2236560.
  16. Jiang, Y. Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter / Y. Jiang, M. Wang // IET Image Processing. – 2014. – Vol. 8, Issue 3. – P. 183-190. – DOI: 10.1049/iet-ipr.2013.0429.
  17. Miao, Z. Information fusion for urban road extraction from VHR optical satellite images / Z. Miao, W. Shi, A. Samat, G. Lisini, P. Gamba // IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. – 2016. – Vol. 9, Issue 5. – P. 1817-1829. – DOI: 10.1109/JSTARS.2015.2498663.
  18. Aulí-Llinàs, F. Bitplane image coding with parallel coefficient processing / F. Aulí-Llinàs, P. Enfedaque, J.C. Moure, V. Sanchez // IEEE Transactions on Image Processing. – 2016. – Vol. 25, Issue 1. – P. 209-219. – DOI: 10.1109/TIP.2015.2484069.
  19. Mandhare, RA. Pixel-level image fusion using brovey transforme and wavelet transform / R.A. Mandhare, P. Upadhyay, S. Gupta // International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. – 2013. – Vol. 2, Issue 6. – P. 2690-2695.
  20. Zhang, S. A novel optimization design approach for Contourlet directional filter banks / S. Zhang, G. Yang, Z. Cheng, H. van de Wetering, C. Ikuta, Y. Nishio // IEICE Electronics Express. – 2014. – Vol. 11, Issue 17. – P. 1-11. – DOI: 10.1587/elex.11.20140556.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20