|
- 2018
一种使用剪切波变换的干涉图滤波算法
|
Abstract:
干涉图小波阈值法滤波未考虑干涉相位统计特性,导致在低相干区域得到的滤波效果不能令人满意。针对这一问题,提出了一种剪切波变换与干涉相位标准差相结合的相位滤波算法。该算法将干涉相位统计特性与剪切波阈值滤波结合起来,利用相位标准差改正滤波阈值以提高滤波效果。此外,为了评价干涉图的滤波效果并为实测数据选择合适的滤波方法提供参考,将模拟干涉图解缠结果的局部均方差分布作为滤波质量评价指标。将此算法与Goldstein滤波、小波滤波、最优方向融合滤波和剪切波软阈值滤波进行比较,结果表明所提算法能更有效地削弱干涉图噪声,同时保留干涉图的细节信息,避免了低相干地区弱滤波问题
[1] | Jin Guowang, Han Xiaoding, Jia Bo, et al. Filtering for InSAR Interferograms by Vector Decomposing and Wavelet Transformation[J]. <em>Geomatics and Information Science of Wuhan University</em>, 2008, 33(2):132-135(靳国旺, 韩晓丁, 贾博,等. InSAR干涉图的矢量分离式小波滤波[J]. 武汉大学学报·信息科学版, 2008, 33(2):132-135) |
[2] | Abdallah W B, Abdelfattah R. Two-Dimensional Wavelet Algorithm for Interferometric Synthetic Aperture Radar Phase Filtering Enhancement[J]. <em>Journal of Applied Remote Sensing</em>, 2015, 9(1):1-17 |
[3] | Fan Hongdong, Deng Kazhong, Pang Lei, et al. Interferogram Filtering Algorithm by Considering Edge Information in DT-CWT Domain[J]. <em>Geoma-tics and Information Science of Wuhan University</em>, 2012, 37(7):810-813(范洪冬, 邓喀中, 庞蕾,等. 结合边缘信息的DT-CWT干涉图滤波算法[J]. 武汉大学学报·信息科学版, 2012,37(7):810-813) |
[4] | Donoho D L. De-noising by Soft-Thresholding[J]. <em>IEEE Transactions on Information Theory</em>, 2002, 41(3):613-627 |
[5] | Gao Guorong, Xu Luping, Feng Dongzhu. Image Denoising Based on the NSST Domain GSM Model[J]. <em>Geomatics and Information Science of Wuhan University</em>,2013, 38(7):778-782(高国荣, 许录平, 冯冬竹. 利用非抽样Shearlet域GSM模型进行图像去噪[J]. 武汉大学学报·信息科学版, 2013, 38(7):778-782) |
[6] | Kutyniok G, Lim W Q. Compactly Supported Shearlets are Optimally Sparse[J]. <em>Journal of Approximation Theory</em>, 2010, 163(11):1564-1589 |
[7] | Gupta D, Anand R S, Tyagi B. Speckle Filtering of Ultrasound Images Using a Modified Non-linear Diffusion Model in Non-subsampled Shearlet Domain[J]. <em>IEEE Transactions on Image Processing</em>, 2015, 9(2):107-117 |
[8] | Wang Q J, Zhang X H, Zhu J J, et al. Designing and Implementation of Variance-Dependent Goldstein Radar Interferogram Filtering[J]. <em>Journal of Central South University</em>, 2014,21(8):3295-3301 |
[9] | Chen Y F, Xu H P. Comparisons of Speckle Noise Filtering Methods on Interferometric Synthetic Aperture Radar Images[J]. <em>Journal of Computers</em>, 2014,9(4):908-915 |
[10] | He Ruyun, Wang Yaonan. InSAR Interferogram Filtering Based on Wavelet Transform[J]. <em>Acta Geodaetica et Cartographica Sinica</em>, 2006, 35(2):128-132(何儒云, 王耀南. 一种基于小波变换的InSAR干涉图滤波方法[J]. 测绘学报, 2006, 35(2):128-132) |
[11] | Yin Hongjie, Li Zhiwei, Ding Xiaoli, et al. Optimal Integration-Based Adaptive Direction Filter for InSAR Interferogram[J]. <em>Journal of Remote Sen-sing</em>, 2009, 13(6):1099-1113(尹宏杰, 李志伟, 丁晓利,等. InSAR干涉图最优化方向融合滤波[J]. 遥感学报, 2009, 13(6):1099-1113) |
[12] | Martinez C L, Canovas X F. SAR Interferometric Phase Noise Reduction Using Wavelet Transform[J]. <em>Electronics Letters</em>, 2001, 37(10):649-651 |
[13] | Franceschetti G, Lanari R. Synthetic Aperture Radar Processing[M]. 2nd ed. Florida:CRC Press Inc, 2016 |
[14] | Li Z W, Ding X L, Huang C, et al. Improved Fil-tering Parameter Determination for the Goldstein Radar Interferogram Filter[J]. <em>ISPRS Journal of Photogrammetry & Remote Sensing</em>, 2008, 63(6):621-634 |
[15] | Song R, Guo H, Liu G, et al. Improved Goldstein SAR Interferogram Filter Based on Adaptive-Neighborhood Technique[J].<em> IEEE Geoscience & Remote Sensing Letters</em>, 2014, 12(1):140-144 |
[16] | Wang Y, Huang H, Dong Z, et al. Modified Patch-Based Locally Optimal Wiener Method for Interfero-metric SAR Phase Filtering[J]. <em>ISPRS Journal of Photogrammetry & Remote Sensing</em>, 2016, 114(4):10-23 |
[17] | Lin X, Li F, Meng D, et al. Nonlocal SAR Interferometric Phase Filtering Through Higher Order Singular Value Decomposition[J]. <em>IEEE Geoscience & Remote Sensing Letters</em>, 2015, 12(4):806-810 |
[18] | Suo Z, Li M, Zhang Q, et al. InSAR Phase Noise Filter in Frequency Domain[J]. <em>IEEE Transactions on Geoscience & Remote Sensing</em>, 2016, 54(2):1185-1195 |
[19] | Patel V M, Easley G R, Chellappa R. Multiscale Directional Filtering of Noisy InSAR Phase Images[C]. The SPIE Conference on Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering, Orlando, Florida, USA, 2010 |