基于分布式壓縮感知的遙感圖像融合算法

劉靜; 李小超; 祝開(kāi)建; 黃開(kāi)宇

doi:10.11999/JEIT161393

基于分布式壓縮感知的遙感圖像融合算法

doi: 10.11999/JEIT161393

1.
(西安交通大學(xué)電子與信息工程學(xué)院西安 710049) ②(西安衛(wèi)星測(cè)控中心宇航動(dòng)力學(xué)國(guó)家重點(diǎn)實(shí)驗(yàn)室西安 710043

基金項(xiàng)目:

CAST 創(chuàng)新基金(J20141110)，國(guó)家自然科學(xué)基金(61573276)，國(guó)家 973 計(jì)劃項(xiàng)目(2013CB329405)

計(jì)量
- 文章訪問(wèn)數(shù): 1495
- HTML全文瀏覽量: 208
- PDF下載量: 315
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-12-23
- 修回日期: 2017-06-15
- 刊出日期: 2017-10-19

Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm

1.
(School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China)

Funds:

The Innovation Foundation of CAST (J20141110), The National Natural Science Foundation of China (61573276), The National 973 Program of China (2013CB329405)

摘要

摘要: 針對(duì)基于壓縮感知(Compressed Sensing, CS)理論的傳統(tǒng)遙感圖像融合算法未能考慮源圖像信息相關(guān)性的特點(diǎn)，該文提出一種基于分布式壓縮感知(Distributed Compressed Sensing, DCS)的遙感圖像融合改進(jìn)算法。通過(guò)DCS的第1聯(lián)合稀疏模型(Joint Sparsity Model-1, JSM-1)提取源圖像低頻信息的公共部分和獨(dú)有部分，再利用獨(dú)有特征添加(UFA)的融合規(guī)則進(jìn)行融合，從而提高融合精度。選取QuickBird衛(wèi)星實(shí)測(cè)圖像數(shù)據(jù)對(duì)該文方法和多個(gè)傳統(tǒng)融合方法進(jìn)行仿真實(shí)驗(yàn)并進(jìn)行評(píng)價(jià)指標(biāo)的對(duì)比，結(jié)果表明該文方法融合性能相對(duì)傳統(tǒng)遙感圖像融合方法都有不同程度的提高。
- 遙感圖像融合 /
- 分布式壓縮感知 /
- 獨(dú)有特征添加 /
- 信息相關(guān)性
Abstract: The conventional Compressed Sensing (CS) based remote sensing image fusion algorithm does not consider the correlation between the source images. In this paper, a novel Distributed CS (DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images. The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1 (JSM-1). The Unique Feature Addition (UFA) rule is then used to improve the fusion performance. In the experiments, the QuickBird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
- Remote sensing image fusion /
- Distributed Compressed Sensing (DCS) /
- Unique Feature Addition (UFA) /
- Information correlation

HTML全文

參考文獻(xiàn)(19)

張曉, 薛月菊, 涂淑琴, 等. 基于結(jié)構(gòu)組稀疏表示的遙感圖像融合[J]. 中國(guó)圖象圖形學(xué)報(bào), 2016, 21(8): 1106-1118. doi: 10.11834/jig.20160815.

ZHANG Xiao, XUE Yueju, TU Shuqin, et al. Remote sensing image fusion based on structural group sparse representation [J]. Journal of Image and Graphics, 2016, 21(8): 1106-1118. doi: 10.11834/jig.20160815.

RAHMANI S, STRAIT M, MERKURJEV D, et al. An adaptive IHS pan-sharpening method[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4): 746-750. doi: 10.1109/LGRS.2010.2046715.

OTAZU X, GONZALEZ-AUDICANA M, FORS O, et al. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods[J]. IEEE Transactions on Geoscience Remote Sensing, 2005, 43(10): 2376-2385. doi: 10.1109/TGRS.2005.856106.

AIAZZI B, ALPARONE L, BARONTI S, et al. MTF- tailored multiscale fusion of high-resolution MS and Pan imagery[J]. Photogrammetric Engineering Remote Sensing, 2006, 72(5): 591-596. doi: 10.14358/PERS.72.5.591.

金益如, 楊學(xué)志, 董張玉, 等. 一種NSST與稀疏表示相結(jié)合的遙感圖像融合算法[J]. 地理與地理信息科學(xué), 2016, 32(2): 60-66. doi: 10.3969/j.issn.1672-0504.2016.02.012.

JIN Yiru, YANG Xuezhi, DONG Zhangyu, et al. A new algorithm for remote sensing image fusion based on NSST and sparse representation[J]. Geography and Geo- Information Science, 2016, 32(2): 60-66. doi: 10.3969/j.issn. 1672-0504.2016.02.012.

ZHU Fuzhen, HE Hongchang, WANG Xiaofei, et al. A new multi-spectral image fusion algorithm based on compressive sensing[C]. The Fifth International Conference on Instrumentation Measurement, Computer, Communication and Control, Qinhuangdao, 2015: 1904-1908. doi: 10.1109/IMCCC.2015.404.

VICINANZA M R, RESTAINO R, VIVONE G, et al. A pansharpening method based on the sparse representation of injected details[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(1): 180-184. doi: 10.1109/LGRS.2014. 2331291.

DUARTE M F, SARVOTHAM S, BARON D, et al. Distributed compressed sensing of jointly sparse signals[C]. Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2005: 1537-1541. doi: 10.1109/ACSSC.2005.1600024.

黃立勤, 陳財(cái)淦. 全景圖拼接中圖像融合算法的研究[J]. 電子與信息學(xué)報(bào), 2014, 36(6): 1292-1298. doi: 10.3724/SP.J.1146. 2013.01220.

HUANG Liqin and CHEN Caigan. Study on image fusion algorithm of panoramic image stitching[J]. Journal of Electronics Information Technology, 2014, 36(6): 1292-1298. doi: 10.3724/SP.J.1146.2013.01220.

YIN Haitao and LI Shutao. Multimodal image fusion with joint sparsity model[J]. Optical Engineering, 2011, 50(6): 409-421. doi: 10.1117/1.3584840.

張穎超, 茅丹, 胡凱. 壓縮傳感理論在心電圖信號(hào)恢復(fù)問(wèn)題上的研究[J]. 計(jì)算機(jī)研究與發(fā)展, 2014, 51(5): 1018-1027. doi: 10.7554/issn1000-1239.2014.20121161.

ZHANG Yingchao, MAO Dan, and HU Kai. ECG signal recovery problem based on compressed sensing theory[J]. Journal of Computer Research and Development, 2014, 51(5): 1018-1027. doi: 10.7554/issn1000-1239.2014.20121161.

CANDES E and TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215. doi: 10.1109/TIT.2005.858979.

LI Shutao and YANG Bin. A new pan-sharpening method using a compressed sensing technique[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 738-746. doi: 10.1109/TGRS.2010.2067219.

VIVONE G, ALPARONE L, CHANUSSOT J, et al. A critical comparison among pansharpening algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2565-2586. doi: 10.1109/TGRS.2014.2361734.

RANCHIN T and WALD L. Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation[J]. Photogrammetric Engineering Remote Sensing, 2000, 66: 49-61.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)