Remote Sensing Image Fusion Based on Optimized Dictionary Learning

Fan LIU; Xiaopeng PEI; Jing ZHANG; Zehua CHEN

doi:10.11999/JEIT180263

Volume 40 Issue 12

Nov. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2804-2811

Fan LIU, Xiaopeng PEI, Jing ZHANG, Zehua CHEN. Remote Sensing Image Fusion Based on Optimized Dictionary Learning[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2804-2811. doi: 10.11999/JEIT180263

Citation:

Fan LIU, Xiaopeng PEI, Jing ZHANG, Zehua CHEN. Remote Sensing Image Fusion Based on Optimized Dictionary Learning[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2804-2811. doi: 10.11999/JEIT180263

Citation:

PDF( 6402 KB)

Remote Sensing Image Fusion Based on Optimized Dictionary Learning

doi: 10.11999/JEIT180263

1.
College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China
2.
College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
3.
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China

Funds: The National Natural Science Foundation of China (61703299, 61402319, 61403273), The Shanxi Province Natural Science Foundation (201601D202044)

Received Date: 2018-03-21
Rev Recd Date: 2018-08-13

Available Online: 2018-08-31

Publish Date: 2018-12-01

Abstract

Abstract

In order to improve the fusion quality of panchromatic image and multi-spectral image, a remote sensing image fusion method based on optimized dictionary learning is proposed. Firstly, K-means cluster is applied to image blocks in the image database, and then image blocks with high similarity are removed partly in order to improve the training efficiency. While obtaining a universal dictionary, the similar dictionary atoms and less used dictionary atoms are marked for further research. Secondly, similar dictionary atoms and less used dictionary atoms are replaced by panchromatic image blocks with the largest difference from the original sparse model to obtain an adaptive dictionary. Furthermore the adaptive dictionary is used to sparse represent the intensity component and panchromatic image, the modulus maxima coefficients in the sparse coefficients of each image blocks are separated to obtain maximal sparse coefficients, and the remaining sparse coefficients are called residual sparse coefficients. Then, each part is fused by different fusion rules to preserve more spectral and spatial detail information. Finally, inverse IHS transform is employed to obtain the fused image. Experiments demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than its counterparts.
- Remote sensing image fusion,
- K-means cluster,
- Adaptive dictionary,
- Sparse represent,
- Fusion rule

FullText(HTML)

References(22)

References

HASSAN G. A review of remote sensing image fusion methods[J]. Information Fusion, 2016, 32(PA): 75–89 doi: 10.1016/j.inffus.2016.03.003

MUFIT C and ABDULKADIR T. Intensity–hue–saturation-based image fusion using iterative linear regression[J]. Journal of Applied Remote Sensing, 2016, 10(4): 045019 doi: 10.1117/1.JRS.10.045019

JI Feng, LI Zeren, CHANG Xia, et al. Remote sensing image fusion method based on PCA and NSCT transform[J]. Journal of Graphics, 2017, 38(2): 247–252 doi: 10.11996/JG.j.2095-302X.2017020247

LI Xu, ZHANG Yiming, GAO Yanan, et al. Using guided filtering to improve gram-schmidt based pansharpening method for GeoEye-1 satellite images[C]. International Conference on Information Systems and Computing Technology, Shanghai, China, 2016: 33–37.

YU Biting, JIA Bo, DING Lu, et al. Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion[J]. Neurocomputing, 2016, 182(C): 1–9 doi: 10.1016/j.neucom.2015.10.084

JORGE N M, XAVIER O, OCTAVI F, et al. Multiresolution-based image fusion with additive wavelet decomposition[J]. IEEE Transactions on Geoscience&Remote Sensing, 1999, 37(3): 1204–1211 doi: 10.1109/36.763274

CHEN Ning, NIU Weiran, ZHANG Jian, et al. Remote sensing image fusion algorithm based on modified Contourlet transform[J]. Journal of Computer Applications, 2015, 35(7): 2015–2019 doi: 10.11772/j.issn.1001-9081.2015.07.2015

刘健, 雷英杰, 邢雅琼, 等. 基于改进型NSST变换的图像融合方法[J]. 控制与决策, 2017, 32(2): 275–280 doi: 10.13195/j.kzyjc.2016.0075

LIU Jian, LEI Yingjie, XING Yaqiong, et al. Image fusion method based on improved NSST transform[J]. Control&Decision, 2017, 32(2): 275–280 doi: 10.13195/j.kzyjc.2016.0075

肖化超, 周诠, 郑小松. 基于IHS变换和Curvelet变换的卫星遥感图像融合方法[J]. 华南理工大学学报 (自然科学版), 2016, 44(1): 58–64 doi: 10.3969/j.issn.1000-565X.2016.01.009

XIAO Huachao, ZHOU Quan, and ZHENG Xiaosong. Remote sensing image fusion based on IHS transform and Curvelet transform[J]. Journal of South China University of Technology(Natural Science Edition), 2016, 44(1): 58–64 doi: 10.3969/j.issn.1000-565X.2016.01.009

刘静, 李小超, 祝开建, 等. 基于分布式压缩感知的遥感图像融合算法[J]. 电子与信息学报, 2017, 39(10): 2374–2381 doi: 10.11999/JEIT161393

LIU Jing, LI Xiaochao, ZHU Kaijian, et al. Remote sensing image fusion algorithm based on distributed compression sensing[J]. Journal of Electronics&Information Technology, 2017, 39(10): 2374–2381 doi: 10.11999/JEIT161393

ALTAN U M, HU Jianwen, and LI Shutao. Remote sensing image fusion method based on nonsubsampled shearlet transform and sparse representation[J]. Sensing&Imaging, 2015, 16(1): 23 doi: 10.1007/s11220-015-0125-0

WANG Jun, PENG Jinye, JIANG Xiaoyue, et al. Remote-sensing image fusion using sparse representation with sub-dictionaries[J]. International Journal of Remote Sensing, 2017, 38(12): 3564–3585 doi: 10.1080/01431161.2017.1302106

The CIFAR-10 dataset [OL]. http://www.cs.toronto.edu/~kriz/cifar.html, 2018.

BRUNO A O and DAVID J F. Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature, 1996, 381(6583): 607–609 doi: 10.1038/381607a0

MICHAL A, MICHAEL E, and ALFRED M B. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311–4322 doi: 10.1109/TSP.2006.881199

RAMIN R and KRISHNAPRASAD P S. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition[C]. Conference on Signals, Systems & Computers, Pacific Grove, USA, 2002: 40–44.

ZHU Xiaoxiang and RICHARD B. A sparse image fusion algorithm with application to pan-sharpening[J].IEEE Transactions on Geoscience&Remote Sensing, 2013, 51(5): 2827–2836 doi: 10.1109/TGRS.2012.2213604

SAMAN J, HASHEMY S M, KOUROSH M, et al. Classification of aquifer vulnerability using k-means cluster analysis[J]. Journal of Hydrology, 2017, 549: 27–37 doi: 10.1016/j.jhydrol.2017.03.060

吴一全, 李立. 利用核模糊聚类和正则化的图像稀疏去噪[J]. 光子学报, 2014, 43(3): 0310001 doi: 10.3788/gzxb20144303.0310001

WU Yiquan and LI Li. Image sparse denoising using kernel fuzzy clustering and regularization[J]. Acta Photonica Sinica, 2014, 43(3): 0310001 doi: 10.3788/gzxb20144303.0310001

刘帆. 基于小波核滤波器和稀疏表示的遥感图像融合[D]. [博士论文], 西安电子科技大学, 2014.

LIU Fan. Remote sensing image fusion based on wavelet kernel filter and sparse representation[D]. [Ph.D. dissertation], Xidian University, 2014.

CARPER W J, THOMAS M L, and RALPH W K. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data[J]. Photogrammetric Engineering&Remote Sensing, 1990, 56(4): 459–467.

MORTEZA G and HASSAN G. Nonlinear IHS: A promising method for Pan-sharpening[J]. IEEE Geoscience&Remote Sensing Letters, 2016, 13(11): 1606–1610 doi: 10.1109/LGRS.2016.2597271

Relative Articles

Supplements(0)

Cited By

Proportional views