Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (1) : 50-56     DOI: 10.6046/gtzyyg.2017.01.08
Technology and Methodology |
Super-resolution fusion method for remote sensing image based on dictionary learning
LI Chengyi, TIAN Shufang
School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China
Download: PDF(3843 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

In consideration of the fact that multi-source remote sensing image fusion is restricted by the existing resolution, the authors propose a super-resolution remote sensing image fusion method based on dictionary learning with sparse representation theory in this paper. The spatial resolution of multispectral images can be promoted to 1 or 2 times higher than the spatial resolution of panchromatic image. Under the framework of the method in remote sensing image fusion, a learning dictionary was established, the redundant dictionary on image sparse representation was used to conduct super-resolution reconstruction implementation. Then the Gram-Schmidt(GS) spectrum sharpening method was used as a fusion rule to obtain super resolution multispectral image fusion. Three experiments were carried out using QuickBird data. The results show that the proposed method is suitable for remote sensing image super-resolution fusion with some advantages in comparison with traditional fusion method, traditional super-resolution method and the other dictionary learning strategy. This paper provides a feasible solution for multi-source remote sensing image fusion, and has referential significance for other fusion methods.

Keywords urban areas extraction      MHSI      HSI      DMSP-OLS     
:  TP751.1  
Issue Date: 23 January 2017
E-mail this article
E-mail Alert
Articles by authors
YANG Xiaonan
XU Yun
TIAN Yugang
Cite this article:   
YANG Xiaonan,XU Yun,TIAN Yugang. Super-resolution fusion method for remote sensing image based on dictionary learning[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 50-56.
URL:     OR

[1] 柴艳妹.多源遥感图像融合技术研究[D].西安:西北工业大学,2004. Chai Y M.The Study on Multi-sensor Image Fusion in Remote Sensing[D].Xi'an:Northwestern Polytechnical University,2004.
[2] 谢茜.像素级遥感图像融合方法研究[D].长沙:中南大学,2009. Xie X.Remote Sensing Image Fusion Method Based on Pixel-level[D].Changsha:Central South University,2009.
[3] Yang J C,Wright J,Huang T S,et al.Image super-resolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[4] 苏秉华,金伟其,牛丽红,等.超分辨率图像复原及其进展[J].光学技术,2001,27(1):6-9. Sun B H,Jin W Q,Niu L H,et al.Super-resolution image restoration and progress[J].Optical Technique,2001,27(1):6-9.
[5] 史祥燕.基于稀疏表示的视频图像超分辨率重建算法研究[D].大连:大连海事大学,2013. Shi X Y.Research on Sparse Representation Based Video Super-resolution Reconstruction Algorithm[D].Dalian:Dalian Maritime University,2013.
[6] Stark H,Oskoui P.High-resolution image recovery from image-plane arrays,using convex projections[J].Journal of the Optical Society of America A,1989,6(11):1715-1726.
[7] Irani M,Peleg S.Improving resolution by image registration[J].CVGIP:Graphical Models and Image Processing,1991,53(3):231-239.
[8] 李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11):1369-1377. Li S T,Wei D.A survey on compressive sensing[J].Acta Automatica Sinica,2009,35(11):1369-1377.
[9] Wan T,Canagarajah N,Achim A.Compressive image fusion[C]//Proceedings of the 15th IEEE International Conference on Image Processing.San Diego,CA:IEEE,2008:1308-1311.
[10] Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[11] Candès E J,Romberg J,Tao T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[12] Candès E J.Compressive sampling[C]//Proceedings of the International Congress of Mathematicians. Madrid:[s.n.],2006:1433-1452.
[13] Tropp J A,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[14] Aharon M,Elad M,Bruckstein A.rmK-SVD:An algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[15] Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61.
[16] 黄健,顾海.基于Gram-Schmidt变换的QuickBird影像融合[C]//地理信息与物联网论坛暨江苏省测绘学会2010年学术年会论文集.无锡:江苏省测绘学会,2010. Huang J,Gu H.QuickBird image fusion based on Gramm-Schmidt transform[C]//Geographic Information and Internet BBS and Surveying and Mapping Institute of Jiangsu Province in 2010 Academic Essays.Wuxi:Jiangsu Surveying and Mapping Institute,2010.
[17] Yang S Y,Sun F H, Wang M,et al.Novel super resolution restoration of remote sensing images based on compressive sensing and example patches-aided dictionary learning[C]//Proceedings of the 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.Xiamen:IEEE,2011:1-6.

[1] WANG Wei, WANG Xinsheng, YAO Chan, JIN Tian, WU Jiayu, SU Wei. Estimation of wheat planting density using UAV image[J]. Remote Sensing for Land & Resources, 2020, 32(4): 111-119.
[2] Dan SHEN, Liang ZHOU, Peian WANG. Identification of poverty based on nighttime light remote sensing data: A case study on contiguous special poverty-stricken areas in Liupan Mountains[J]. Remote Sensing for Land & Resources, 2019, 31(2): 157-163.
[3] YANG Xiaonan, XU Yun, TIAN Yugang. A study of urban area extraction with the modified human settlement index[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 127-134.
[4] CHOU Li-Ming, MENG Ji-Hua, WU Bing-Fang, CHEN Xue-Yang, DU Xin, ZHANG Fei-Fei. Research on Standard Preprocessing Flow for HJ-1A HSI Level 2 Data Product[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 77-82.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech