MS and PAN Image Fusion Algorithm Based on PST Phase Constraint and Sparse Representation
WANG Xianghai1,2, BAI Shifu1, LI Zhi1, SONG Ruoxi2, TAO Jingzhe2
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116081; 2.College of Urban and Environmental Sciences, Liaoning Normal Universtiy, Dalian 116029
Abstract In the remote sensing image fusion based on multi-spectral(MS) image and panchromatic(PAN) image, effective extracting the texture feature information of PAN and injecting targeted information into MS image are crucial to the high quality of image fusion. Therefore, the MS and PAN image pansharpening algorithm based on phase constraint of phase stretch transform(PST) and sparse representation is proposed in this paper. Firstly, the MS and PAN images are filtered by Gaussian filter. For the low and medium frequency information, the fusion weight constraint is obtained by the phase difference of high frequency based on the sensitivity of the PST phase difference to the edge and texture region in the image. For the high frequency information, a training dictionary is obtained by learning the high frequency information of the PAN image, and the dictionary is used to sparsely represent and fuse the high frequency information of MS and PAN images, therefore the accuracy of high frequency fusion is improved. The proposed algorithm overcomes the poor fusion effect of traditional fusion methods on the edge texture region and the distortion of spectral information, achieves better fusion result. A large number of simulation experiments verify the effectiveness of the proposed method.
Fund:Supported by National Natural Science Foundation of China(No.41671439,61402214), Program for Liaoning Innovation Research Team in University(No.LT2017013)
Corresponding Authors:
(WANG Xianghai(Corresponding author), Ph.D., professor. His research interests include remote sensing image processing and multimedia information processing.)
About author:: (BAI Shifu, master student. His research interests include remote sensing image processing.)(LI Zhi, master student. His research inte-rests include image segmentation and remote sensing image fusion.)(SONG Ruoxi, Ph.D. candidate. Her research interests include remote sensing image processing and mathematical modeling.)(TAO Jingzhe, Ph.D. candidate. His research interests include remote sensing fusion and super-resolution reconstruction.)
[1] POHL C, CAN GENDEREN L. Remote Sensing Image Fusion: A Practical Guide. Boca Raton, USA: CRC Press, 2017. [2] GHASSEMIAN H. A Review of Remote Sensing Image Fusion Me-thods. Information Fusion, 2016, 32: 75-89. [3] MENG X C, SHEN H F, LI H F, et al. Review of the Pansharpe-ning Methods for Remote Sensing Images Based on the Idea of Meta-Analysis: Practical Discussion and Challenges. Information Fusion, 2019, 46: 102-113. [4] ZHOU Y W, YANG P L, CHEN Q, et al. Pan-Sharpening Model Based on MTF and Variational Method. Acta Automatica Sinica, 2015, 41(2): 342-352. [5] LUO X Q, ZHANG Z C, ZHANG B C, et al. Image Fusion with Contextual Statistical Similarity and Nonsubsampled Shearlet Transform. IEEE Sensors Journal, 2017, 17(6): 1760-1771. [6] VIVONE G, ALPARONE L, CHANUSSOT J, et al. A Critical Comparison among Pansharpening Algorithms. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2565-2586. [7] NIELSEN M M. Remote Sensing for Urban Planning and Management: The Use of Window-Independent Context Segmentation to Extract Urban Features in Stockholm. Computers, Environment and Urban Systems, 2015, 52: 1-9. [8] ZHANG Y Z, ZHANG H S, LIN H. Improving the Impervious Surface Estimation with Combined Use of Optical and SAR Remote Sensing Images. Remote Sensing of Environment, 2014, 141: 155-167. [9] MA K D, DUANMU Z F, YEGANEH H, et al. Multi-exposure Image Fusion by Optimizing a Structural Similarity Index. IEEE Transactions on Computational Imaging, 2018, 4(1): 60-72. [10] LI H, SONG Y L, CHEN C L P. Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(9): 5302-5312. [11] HAN C, ZHANG H Y, CAO C X, et al. A Remote Sensing Image Fusion Method Based on the Analysis Sparse Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(1): 439-453. [12] CANDS E J, WAKIN M B. An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 2008, 25(2): 14-20 [13] 邵文泽,韦志辉.压缩感知基本理论:回顾与展望.中国图象图形学报, 2012, 17(1): 1-12. (SHAO W Z, WEI Z H. Advances and Perspectives on Compressed Sensing Theory. Journal of Image and Graphics, 2012, 17(1): 1-12.) [14] 张良培,李家艺.高光谱图像稀疏信息处理综述与展望.遥感学报, 2016, 20(5): 1091-1101. (ZHANG L P, LI J Y. Development and Prospect of Sparse Representation-Based Hyperspectral Image Processing and Analysis. Journal of Remote Sensing, 2016, 20(5): 1091-1101.) [15] YU X C, CAO G Y, XU J D, et al. Remote Sensing Image Fusion Based on Sparse Representation // Proc of the IEEE Geoscience and Remote Sensing Symposium. Washington, USA: IEEE, 2014: 2858-2861. [16] LIU Y, LIU S P, WANG Z F. A General Framework for Image Fusion Based on Multi-scale Transform and Sparse Representation. Information Fusion, 2015, 24: 147-164. [17] 陈木生.结合NSCT和压缩感知的红外与可见光图像融合.中国图象图形学报, 2016, 21(1): 39-44. (CHEN M S. Image Fusion of Visual and Infrared Image Based on NSCT and Compressed Sensing. Journal of Image and Graphics, 2016, 21(1): 39-44.) [18] WANG J, PENG J Y, JIANG X Y, et al. Remote-Sensing Image Fusion Using Sparse Representation with Sub-dictionaries. International Journal of Remote Sensing, 2017, 38(12): 3564-3585. [19] 尹 雯,李元祥.基于稀疏表示的遥感影像融合方法.光学学报, 2013, 33(4). DOI:10.3788／AOS201333.0428003. (YIN W, LI Y X. Remote Sensing Image Fusion Based on Sparse Representation. Acta Optica Sinica, 2013, 33(4). DOI:10.3788／AOS201333.0428003.) [20] ASGHARI M H, JALALI B. Edge Detection in Digital Images Using Dispersive Phase Stretch Transform. International Journal of Biomedical Imaging, 2015. DOI: 10.1155/2015/687819. [21] ILOVITSH T, JALALI B, ASGHARI M H, et al. Phase Stretch Transform for Super-Resolution Localization Microscopy. Biomedi-cal Optics Express, 2016, 7(10): 4198-4209. [22] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Transactions on Image Processing, 2006, 54(11): 4311-4322. [23] PATI Y C,REZAIIFAR R,KRISHNAPRASAD P S. Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition // Proc of the 27th Asilomar Conference on Signals, Systems and Computers. Berlin, Germany: Springer, 1993: 40-44. [24] OJHA C, FUSCO A, MANUNTA M. Denoising of Full Resolution Differential SAR Interferogram Based on K-SVD Technique // Proc of the IEEE International Geoscience and Remote Sensing Symposium. Washington, USA: IEEE, 2015: 2461-2464. [25] MARUTURI H, HIMA B C, SATYA P K. Image Fusion with Biorthogonal Wavelet Transform Based on Maximum Selection and Region Energy // Proc of the IEEE Conference on Computer Communication and Informatics. Washington, USA: IEEE, 2014. DOI: 10.1109/ICCCI.2014.6921720. [26] KAUR H, RANI J. Image Fusion on Digital Images Using Lapla- cian Pyramid with DWT // Proc of the 3rd International Conference on Image Information Processing. Berlin, Germany: Springer, 2015: 393-398. [27] MANU C S, JIJI C V. A Novel Remote Sensing Image Fusion Algorithm Using ICA Bases // Proc of the 8th IEEE International Conference on Advances in Pattern Recognition. Washington, USA: IEEE, 2015. DOI: 10.1109/ICAPR.2015.7050690. [28] LIU Y, WANG Z F. Simultaneous Image Fusion and Denoising with Adaptive Sparse Representation. IET Image Processing, 2015, 9(5): 347-357. [29] LI H, WU X J, KITTLER J. Infrared and Visible Image Fusion Using a Deep Learning Framework[C/OL]. [2018-08-20]. https://arxiv.org/pdf/1804.06992.pdf.
2019, Vol. 32 
