小波系数插值支持下的遥感影像混合像元分解

doi:10.3969/j.issn.0255-8297.2012.06.009

应用科学学报 ›› 2012, Vol. 30 ›› Issue (6): 613-618.doi: 10.3969/j.issn.0255-8297.2012.06.009

小波系数插值支持下的遥感影像混合像元分解

李熙1，陈锋锐2，于之锋1,3

1. 武汉大学测绘遥感信息工程国家重点实验室，武汉430079
2. 河南大学环境与规划学院，河南开封475004
3. 杭州师范大学遥感与地球科学研究院，杭州311121

收稿日期:2011-11-24 修回日期:2012-01-22 出版日期:2012-11-27 发布日期:2012-01-22
通信作者: 李熙，博士，讲师，研究方向：遥感图像处理，E-mail：lixi@whu.edu.cn
作者简介:李熙，博士，讲师，研究方向：遥感图像处理，E-mail：lixi@whu.edu.cn
基金资助:
国家“863” 高技术研究发展计划基金(No.2012AA12A306)；国家自然科学基金(No.41101413)；教育部博士点基金(No.20110141120073)；中央高校基本科研业务费专项资金(No.904275839)；杭州师范大学遥感与地球科学研究院开放基金(No.PDKF2010YG06)；中国博士后科学基金(No.2012M511571)资助

Spectral Unmixing of Remote Sensing Images Using Interpolation of Wavelet Coefficients

LI Xi1, CHEN Feng-rui2, YU Zhi-feng1,3

1．State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2. College of Environment and Planning, Henan University, Kaifeng 475004, Henan Province, China
3．Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China

Received:2011-11-24 Revised:2012-01-22 Online:2012-11-27 Published:2012-01-22

摘要/Abstract

摘要： 针对遥感混合像元分解中不能有效利用空间邻域信息的问题，提出一种基于超分辨率重建的分解方法. 通过小波系数双线性插值获得遥感影像的超分辨率影像，对超分辨率影像进行监督分类生成超分辨率分类图，最后通过窗口统计得到原始分辨率下各地物的丰度图. 广州城区的模拟TM遥感影像试验表明，该方法的分解精度
在3种方法中最优，能够较充分利用空间邻域信息，提高混合像元分解精度，为混合像元分解提供了新的途径.

关键词: 遥感, 混合像元, 小波变换, 超分辨率重建, 双线性插值

Abstract: This paper proposes a wavelet coefficient interpolation method, which uses the neighboring information in the spatial domain for spectral unmixing of remote sensing images. A super-resolution image is first generated using bilinear interpolation of wavelet coefficients. The new image is then classified to derive
a super-resolution classification map. Finally, an abundance map at the original spatial resolution is obtained using a counting window on the super-resolution classification map. This way, the original image is unmixed. A simulated TM image of Guangzhou City is used to verify the proposed method. It is found that the method performs best among three methods as it can make use of neighboring information in the space to improve unmixing accuracy.

Key words: remote sensing, mixed pixel, wavelet transformation, super resolution reconstruction, bilinear interpolation

中图分类号:

P751.1

李熙1，陈锋锐2，于之锋1,3. 小波系数插值支持下的遥感影像混合像元分解[J]. 应用科学学报, 2012, 30(6): 613-618.

LI Xi1, CHEN Feng-rui2, YU Zhi-feng1,3. Spectral Unmixing of Remote Sensing Images Using Interpolation of Wavelet Coefficients[J]. Journal of Applied Sciences, 2012, 30(6): 613-618.

参考文献

[1]CHEN C H, HO P G P. Statistical pattern recognition in remote sensing [J]. Pattern Recognition, 2008, 41(9): 2731-2741.

[2]DURAN O, PETROU, M. Spectral unmixing with negative and superunity abundances for subpixel anomaly detection [J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(1): 152-156.

[3]ECHES O, DOBIGEON, N, MAILHES C. Bayesian estimation of linear mixtures using the normal compositional model [J]. IEEE Transactions on Image Processing, 2010, 19(6): 1403-1413.

[4]CHEN J, JIA X P, YANG W, MATSUSHITA B. Generalization of subpixel analysis for hyperspectral data with flexibility in spectral similarity measures [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(7): 2165-2171.

[5]RAKSUNTORN N, DU Q. Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment [J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4): 836-840.

[6]LEE S, LATHROP R G. Subpixel analysis of Landsat ETM+ using self-organizing map (SOM) neural networks for urban land cover characterization [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1642-1654.

[7]PLAZA J, PLAZA A, PEREZ R, MARTINEZ P. On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 42(11): 3032-3045.

[8]WANG L, JIA X P. Integration of soft and hard classifications using extended support vector machines [J]. IEEE Geoscience and Remote Sensing Letter, 2009, 6(3): 543-547.

[9]TAKEDA H, FARSIU S, MILANFAR P. Kernel regression for image processing and reconstruction [J]. IEEE Transactions on Image Processing, 2007, 16(2): 349-366.

[10]YANG J C, WRIGHT J, HUANG T S, MA Y. Image super-resolution via sparse representation [J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.

[11]KIM K I, KWON Y. Single-image super-resolution using sparse regression and natural image prior [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(6): 1127-1133.

[12]刘笑宙，涂国防. 小波双线性插值应用于光学遥感图像 [J]. 中国科学院研究生院学报, 2003, 20(1): 39-43.

LIU Xiaozhou, TU Guofang. Wavelet bilinear interpolation in remote sensing image [J]. Journal of the Graduate School of the Chinese Academy of Sciences, 2003, 20(1): 39-43. (in Chinese)

[13]Liu W G, Wu E Y. Comparison of non-linear mixture models: sub-pixel classification [J]. Remote Sensing of Environment, 2005, 94(2): 145-154.

[14]POWELL R L, ROBERTS D A, DENNISON P E, HESS L L. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil [J]. Remote Sensing of Environment, 2007, 106(2): 253-267.

[15]BEN R Z, FARAH I R, MERCIER G, SOLAIMAN B. A new method to change illumination effect reduction based on spectral angle constraint for hyperspectral image unmixing [J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1110-1114.

小波系数插值支持下的遥感影像混合像元分解

Spectral Unmixing of Remote Sensing Images Using Interpolation of Wavelet Coefficients

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