基于单亲遗传算法的SAR图像混合像元分解
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摘要
已有的遥感影像混合像元分解理论方法都要求遥感影像的通道数目大于地物种类,而合成孔径雷达(SAR)的自身特点决定了SAR图像不可能有过多的通道数目,为解决SAR图像地物种类大于通道数目情况下的混合像元分解问题,本文基于单亲遗传算法提出了一种新的混合像元分解方法,创建了一种新的染色体编码方式及进化迭代方式,新算法很好地实现混合像元的分解,可以分解出比通道数目更多的地物种类.并从北京地区ENVISAT-ASAR图像中截取天安门附近区域作为数据源进行实验,实验结果表明了本文算法的正确性和有效性.
The existing theories and methods used for remote sensing images mixed pixel decomposition all require that the number of image channels should be more than that of the ground types, but the characteristics of the Synthetic Aperture Radar (SAR) hinder the SAR image from having excessive channels. To solve the problem in which the SAR images mixed pixel decomposition in circumstance of ground types being more than channels number, we propose a new method for mixed pixels' decomposition based on Partheno-Genetic Algorithm. In our algorithm, we establish a new chromosome coding method and an evolution iterative method. The new algorithm can realize the ideal mixed pixel decomposition and decompose more ground types than the number of channels. We intercept the area of the Tiananmen vicinity from the Beijing ENVISAT-ASAR image as our experiment data source and compare the result with the real ground feature. The experiment result shows that our algorithm is correct and effective.
The existing theories and methods used for remote sensing images mixed pixel decomposition all require that the number of image channels should be more than that of the ground types, but the characteristics of the Synthetic Aperture Radar (SAR) hinder the SAR image from having excessive channels. To solve the problem in which the SAR images mixed pixel decomposition in circumstance of ground types being more than channels number, we propose a new method for mixed pixels' decomposition based on Partheno-Genetic Algorithm. In our algorithm, we establish a new chromosome coding method and an evolution iterative method. The new algorithm can realize the ideal mixed pixel decomposition and decompose more ground types than the number of channels. We intercept the area of the Tiananmen vicinity from the Beijing ENVISAT-ASAR image as our experiment data source and compare the result with the real ground feature. The experiment result shows that our algorithm is correct and effective.
引文
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[10] 彭望琭,余先川,周涛等译.遥感与图像解译.第四版.北京:电子工业出版社,2003 Thomas M L, Ralph W K. Remote Sensing and Image Interpretation. Fourth Edition. John Wiley & Sons Inc. ,2000
[11] Chao Ding, Ye Cheng, Miao He. Two-level genetic algorithm for clustered traveling salesman problem with application in large-scale TSPs. Tsinghua Science & Technology ,2007,12(4) :459-461
[12] Zhang Xiaowei, Liu Sanyang. A new interval-genetic algorithm. Third International Conference on Natural Computation ,2007,24(4) :193-197
[13] Zhao X L, Althouse M L G. Least squares subspace projection approach to mixed pixel classification for hyperspectral images. IEEE Trans.on Geoscience and Remote Sensing,1998, 36(3) :898-912
[14] Goldberg D E. Genetic Algorithm, in Search, Optimize and Machine Learning. New York: Addiso Wes-ley,1993． 372-385
[15] Vicente Valls, Francisco Ballestin, Sacramento Quintanilla. A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research ,2008, 185(2) : 495-508
[16] 陈慧琴,刘刚.用整数编码的单亲遗传算法求解组合优化问题.武汉理工大学学报:交通科学与工程版,2003,27(2) :241-243 Chen H Q, Liu G. Integer coding based on single parent genetic algorithm for optimization of combination problem. Journal of Wuhan University of Technology ( Transportation Science & Engineering) (in Chinese) ,2003,27(2) :241-243
[17] Harold Szu,Ivica Kopriva. Artificial neural networks for noisy image super-resolution. Optics Communications ,2001,198(1-3) :71-81
[18] Brian A Telfer, Harold H Szu. Energy functions for minimizing misclassification error with minimum-complexity networks. Neural Networks ,1994,7(5) :80-89
[2] Cheng-Ⅰ Chang. Hsuan Ren, Chem-Chi Chang, et al. Estimation of subpixel target size for remotely sensed imagery. IEEE Transaction on Geoscience and Remote Sensing, 2004． 42(6) :1309-1320
[3] 单新建,李建华,马超.昆仑山口西M_S8． 1级地震地表破裂带高分辨率卫星影像特征研究.地球物理学报.2005,48(2) :321-326 Shan X J, Li J H, Ma C. Study on the feature of surface rupture zone of the west of Kunlunshan pass earthquake (M_S8． 1) with high spatial resolution satellite images. Chinese J. Geophys. (in Chinese). 2005, 48(2) :321-326
[4] 戴前伟,冯德山,何继善.Kirchhoff偏移法在探地雷达正演图像处理中的应用.地球物理学进展,2005,20(3) :849-853 Dai Q W,Feng D S,He J S. The application of Kirchhoff's migration method in the image processing of the ground penetrating radar forward simulate. Progress in Geophysics (in Chinese), 2005,20(3) :849-853
[5] 杨桄,陈克雄,周脉鱼等.SAR图像中目标的检测和识别研究进展.地球物理学进展,2007,22(2) :617-621 Yang G, Chen K X, Zhou M Y, et al. Study evolution of detection and recognition on target in SAR image. Progress in Geophysics (in Chinese) ,2007,22(2) :617-621
[6] 冯伟,吴如山,马在田.单向传播子的局部平面波分解以及在定向照明分析和目标特征成像中的应用.地球物理学进展,2007,22(1) :171-178 Feng W,Wu R Sh, Ma Z T. Local plane wave decomposition of one-way propagator and the applications to directional illumination analysis and target-structure oriented imaging. Progress in Geophysics (in Chinese), 2007,22(1) :171-178
[7] Shaw G, Burke H. Spectral imaging for remote sensing. Lincoln Laboratory Journal, 2003,14(1) :3-28
[8] Tatiana M, Kuplich T M. Classifying regenerating forest stages in Amazonia using remotely sensed images and a neural network. Forest Ecology and Management,2006,234(1-3) :1-9
[9] Zhang Y, Shao M Z. Decomposing mixed pixels by means of RBF neural network. Journal of Remote Sensing, 2004, 7:285-289
[10] 彭望琭,余先川,周涛等译.遥感与图像解译.第四版.北京:电子工业出版社,2003 Thomas M L, Ralph W K. Remote Sensing and Image Interpretation. Fourth Edition. John Wiley & Sons Inc. ,2000
[11] Chao Ding, Ye Cheng, Miao He. Two-level genetic algorithm for clustered traveling salesman problem with application in large-scale TSPs. Tsinghua Science & Technology ,2007,12(4) :459-461
[12] Zhang Xiaowei, Liu Sanyang. A new interval-genetic algorithm. Third International Conference on Natural Computation ,2007,24(4) :193-197
[13] Zhao X L, Althouse M L G. Least squares subspace projection approach to mixed pixel classification for hyperspectral images. IEEE Trans.on Geoscience and Remote Sensing,1998, 36(3) :898-912
[14] Goldberg D E. Genetic Algorithm, in Search, Optimize and Machine Learning. New York: Addiso Wes-ley,1993． 372-385
[15] Vicente Valls, Francisco Ballestin, Sacramento Quintanilla. A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research ,2008, 185(2) : 495-508
[16] 陈慧琴,刘刚.用整数编码的单亲遗传算法求解组合优化问题.武汉理工大学学报:交通科学与工程版,2003,27(2) :241-243 Chen H Q, Liu G. Integer coding based on single parent genetic algorithm for optimization of combination problem. Journal of Wuhan University of Technology ( Transportation Science & Engineering) (in Chinese) ,2003,27(2) :241-243
[17] Harold Szu,Ivica Kopriva. Artificial neural networks for noisy image super-resolution. Optics Communications ,2001,198(1-3) :71-81
[18] Brian A Telfer, Harold H Szu. Energy functions for minimizing misclassification error with minimum-complexity networks. Neural Networks ,1994,7(5) :80-89
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