Remote sensing image fusion based on adaptively weighted joint detail injection

IEEE Access

Volumn 6, Issue , 2018, Pages 6849-6864

  Yang, Yong ^a   Wu, Lei ^a   Huang, Shuying ^a   Wan, Weiguo ^b   Que, Yue ^a  

^a JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^b Chonbuk National University   (South Korea)

Author keywords

Detail injection scheme; Multiscale decomposition; Remote sensing image fusion; Sparse representation

Indexed keywords

IMAGE PROCESSING; MATHEMATICAL TRANSFORMATIONS; MULTIRESOLUTION ANALYSIS; REMOTE SENSING; SENSORS; WAVELET TRANSFORMS;

INJECTION SCHEMES; MULTI-SCALE DECOMPOSITION; REMOTE SENSING IMAGES; SPARSE REPRESENTATION; SPATIAL RESOLUTION;

IMAGE FUSION;

EID: 85041201614     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2018.2791574     Document Type: Article

References (57)

1. Q. Wei, N. Dobigeon, and J. Y. Tourneret, Fast fusion of multi-band images based on solving a Sylvester equation, '' IEEE Trans. Image Process., vol. 24, no. 11, pp. 4109-4121, Nov. 2015.
2. F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, Model-based fusion of multi-And hyperspectral images using PCA and wavelets, '' IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2652-2663, May 2015.
3. L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L. M. Bruce, Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest, '' IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3012-3021, Oct. 2007.
4. N. B. Chang, K. Bai, S. Imen, and C. F. Chen, Multisensor satellite image fusion and networking for all-weather environmental monitoring, '' IEEE Syst. J., to be publisheddoi:10.1109/JSYST.2016.2565900.2016.
5. C. Pohl and J. V. Genderen, Remote sensing image fusion: An update in the context of digital earth, '' Int. J. Digit. Earth, vol. 7, no. 2, pp. 158-172, 2014.
6. H. Ghassemian, A review of remote sensing image fusion methods, '' Inf. Fusion, vol. 32, pp. 75-89, Nov. 2016.
7. L. Zhang, H. Shen, W. Gong, and H. Zhang, Adjustable model-based fusion method for multispectral and panchromatic images, '' IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 6, pp. 1693-1704, Dec. 2012.
8. T. M. Tu, S. C. Su, H. C. Shyu, and P. S. Huang, A new look at IHS-likeimage fusion methods, '' Inf. Fusion, vol. 2, no. 3, pp. 177-186, Sep. 2001.
9. Z.Wang, D. Ziou, C. Armenakis, D. Li, and Q. Li, A comparative analysis of image fusion methods, '' IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1391-1402, Jun. 2005.
10. C. A. Laben and B. V. Brower, Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening, '' U.S. Patent 6 011 875, Jan. 4, 2000.
11. Y. Zhang, Problems in the fusion of commercial high-resolution satellite images as well as Landsat 7 images and initial solutions, '' in Proc. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. 34. 2002, pp. 587-592.
12. X. Zhou, J. Liu, S. Liu, L. Cao, Q. Zhou, and H. Huang, A GIHSbased spectral preservation fusion method for remote sensing images using edge restored spectral modulation, '' ISPRS J. Photogramm. Remote Sens., vol. 88, no. 2, pp. 16-27, 2014.
13. J. Choi, K.Yu, and Y. Kim, Anewadaptive component-substitution-based satellite image fusion by using partial replacement, '' IEEE Trans. Geosci. Remote Sens., vol. 49, no. 1, pp. 295-309, Jan. 2011.
14. Y. Yang, W. Wan, S. Huang, and F. Yuan, Remote sensing image fusion based on adaptive IHS and multiscale guided filter, '' IEEE Access, vol. 4, pp. 4573-4582, 2016.
15. D. Sylla, A. Minghelli-Roman, P. Blanc, A. Mangin, and O. D. HembiseFanton, Andon, Fusion of multispectral images by extension of the pan-sharpening ARSIS method, '' IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 5, pp. 1781-1791, May 2014.
16. A. P. James and B. V. Dasarathy, Medical image fusion: A survey of the state of the art, '' Inf. Fusion, vol. 19, no. 3, pp. 4-19, 2013.
17. S. Li, J. T. Kwok, and Y. Wang, Using the discrete wavelet frame transform to merge landsat TM and SPOT panchromatic images, '' Inf. Fusion, vol. 3, no. 1, pp. 17-23, 2002.
18. J. Nunez, X. Otazu, O. Fors, A. Prades, V. Pala, and R. Arbiol, Multiresolution-based image fusion with additive wavelet decomposition, '' IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1204-1211, May 1999.
19. A. L. da Cunha, J. Zhou, and M. N. Do, The nonsubsampled contourlet transform: Theory, design, and applications, '' IEEE Trans. Image Process., vol. 15, no. 10, pp. 3089-3101, Oct. 2006.
20. M. Yin, W. Liu, X. Zhao, Y. Yin, and Y. Guo, A novel image fusion algorithm based on nonsubsampled shearlet transform, '' Opt.-Int. J. Light Electron Opt., vol. 125, no. 10, pp. 2274-2282, 2014.
21. L. Alparone, S. Baronti, B. Aiazzi, and A. Garzelli, Spatial methods for multispectral pansharpening: Multiresolution analysis demystified, '' IEEE Trans. Geosci. Remote Sens., vol. 54, no. 5, pp. 2563-2576, May 2016.
22. G. Vivone et al., A critical comparison among pansharpening algorithms, '' IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2565-2586, May 2015.
23. X. Otazu, M. Gonzalez-Audicana, O. Fors, and J. Nunez, Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods, '' IEEE Trans. Geosci. Remote Sens., vol. 43, no. 10, pp. 2376-2385, Oct. 2005.
24. M. Ghahremani and H. Ghassemian, Remote-sensing image fusion based on curvelets and ICA, '' Int. J. Remote Sens., vol. 36, no. 16, pp. 4131-4143, 2015.
25. V. P. Shah, N. H. Younan, and R. L. King, An effcient pan-sharpening method via a combined adaptive PCA approach and contourlets, '' IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1323-1335, May 2008.
26. W. Huang, L. Xiao, Z. Wei, H. Liu, and S. Tang, A new pan-sharpening method with deep neural networks, '' IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 1037-1041, May 2015.
27. G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, Pansharpening by convolutional neural networks, '' Remote Sens., vol. 8, no. 7, pp. 594:1-594:22, 2016.
28. D. Fasbender, J. Radoux, and P. Bogaert, Bayesian data fusion for adaptable image pansharpening, '' IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1847-1857, Jun. 2008.
29. M. Joshi and A. Jalobeanu, MAP estimation for multiresolution fusion in remotely sensed images using an IGMRF prior model, '' IEEE Trans. Geosci. Remote Sens., vol. 48, no. 3, pp. 1245-1255, Mar. 2010.
30. M. Guo, H. Zhang, J. Li, L. Zhang, and H. Shen, An online coupled dictionary learning approach for remote sensing image fusion, '' IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1284-1294, Apr. 2014.
31. J. Cheng, H. Liu, T. Liu, F.Wang, and H. Li, Remote sensing image fusion via wavelet transform and sparse representation, '' ISPRS J. Photogramm. Remote Sens., vol. 104, pp. 158-173, 2015.
32. S. Li, H. Yin, and L. Fang, Remote sensing image fusion via sparse representations over learned dictionaries, '' IEEE Trans. Geosci. Remote Sens., vol. 51, no, . 9 pp. 4779-4789, Sep. 2013.
33. X. X. Zhu and R. Bamler, A sparse image fusion algorithm with application to pan-sharpening, '' IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2827-2836, May 2013.
34. C. Han, H. Zhang, C. Gao, and C. Jiang, A remote sensing image fusion method based on the analysis sparse model, '' IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 1, pp. 439-453, Jan. 2016.
35. C. Thomas, T. Ranchin, L. Wald, and J. Chanussot, Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics, '' IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1301-1312, May 2008.
36. T.-M. Tu, P. S. Huang, C.-L. Hung, and C.-P. Chang, A fast intensityhue- saturation fusion technique with spectral adjustment for IKONOS imagery, '' IEEE Geosci. Remote Sens. Lett., vol. 1, no. 4, pp. 309-312, Oct. 2004.
37. K. He, J. Sun, and X. Tang, Guided image filtering, '' IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397-1409, Jun. 2013.
38. M. Elad, M. A. T. Figueiredo, and Y. Ma, On the role of sparse and redundant representations in image processing, '' Proc. IEEE, vol. 98, no. 6, pp. 972-982, Jun. 2010.
39. S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, '' IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
40. M. Aharon, M. Elad, and A. Bruckstein, K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation, '' IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
41. A.-U. Moonon, J. Hu, and S. Li, Remote sensing image fusion method based on nonsubsampled shearlet transform and sparse representation, '' Sens. Imag., vol. 16, no. 1, pp. 1-18, Nov. 2015.
42. L. Wald, T. Ranchin, and M. Mangolini, Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images, '' Photogramm. Eng. Remote Sens., vol. 63, no. 6, pp. 691-699, 1997.
43. Remote Sensing Image Fusion Datasets. Accessed: Sep. 17, 2015. [Online]. Available: http://www.datatang.com/data/43234.
44. QuickBird Datasets. Accessed: Jul. 12, 2016. [Online]. Available: http://www.glcf.umiacs.umd.edu/data/.
45. IKONOS Datasets. Accessed: Mar. 22, 2016. [Online]. Available: http://www.isprs.org/data/ikonos-hobart/default.aspx.
46. Z.Wang and A. C. Bovik, A universal image quality index, '' IEEE Signal Process. Lett., vol. 9, no. 3, pp. 81-84, Mar. 2002.
47. Y. Yang, S. Tong, S. Huang, and P. Lin, Multifocus image fusion based on NSCT and focused area detection, '' IEEE Sensors J., vol. 15, no. 5, pp. 2824-2838, May 2015.
48. T. Ranchin and L. Wald, Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation, '' Photogramm. Eng. Remote Sens., vol. 66, no. 1, pp. 49-61, Jan. 2000.
49. L. Wald, Quality of high resolution synthesised images: Is there a simple criterion?'' in Proc. Int. Conf. Fusion Earth Data, Jan. 2000, pp. 99-103.
50. Z. H. Nezhad, A. Karami, R. Heylen, and P. Scheunders, Fusion of hyperspectral and multispectral images using spectral unmixing and sparse coding, '' IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 6, pp. 2377-2389, Jun. 2016.
51. L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L. M. Bruce, Comparison of pansharpening algorithms: Outcome of the 2006 GRSS data fusion contest, '' IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3012-3021, Oct. 2007.
52. L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, and M. Selva, Multispectral and panchromatic data fusion assessment without reference, '' Photogramm. Eng. Remote Sens., vol. 74, no. 2, pp. 193-200, Feb. 2008.
53. B. Aiazzi, S. Baronti, and M. Selva, Improving component substitution pansharpening through multivariate regression of MS+Pan data, '' IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3230-3239, Oct. 2007.
54. Y. Leung, J. Liu, and J. Zhang, An improved adaptive intensity-huesaturation method for the fusion of remote sensing images, '' IEEE Geosci. Remote Sens. Lett., vol. 11, no. 5, pp. 985-989, May 2014.
55. N. H. Kaplan and I. Erer, Bilateral -ltering-based enhanced pansharpening of multispectral satellite images, '' IEEE Geosci. Remote Sens Lett., vol. 11, no. 11, pp. 1941-1945, Nov. 2014.
56. X. Kang, S. Li, and J. A. Benediktsson, Pansharpening with matting model, '' IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 5088-5099, Aug. 2014.
57. Y. Yang, W. Wan, S. Huang, P. Lin, and Y. Que, A novel pan-sharpening framework based on matting model and multiscale transform, '' Remote Sens., vol. 9, no. 4, pp. 391:1-391:21, 2017.