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.
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
.
DOI: 10.3969/j.issn.0255-8297.2012.06.009
[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.