Ojha Chandrakanta, Fusco Adele, Pinto Innocenzo M
School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85281, USA.
CNR IREA, Via Diocleziano 328, 80124 Naples, Italy.
Sensors (Basel). 2019 Jun 14;19(12):2684. doi: 10.3390/s19122684.
This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in [ - π , π ] , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.
本文探讨了基于训练字典上的稀疏和冗余表示的合成孔径雷达（SAR）干涉测量中干涉噪声降低的问题。其思路是在干涉数据上使用一种算法来获得合适的字典，以便有效地提取相位图像内容。我们在模拟和实际干涉数据上都实施了该策略，以验证我们的方法。对于合成数据，比较了三种不同的训练字典，即从数据中提取的字典、通过在[-π,π]上均匀随机分布获得的字典以及由离散余弦变换构建的字典。此外，类似的策略计划已应用于实际干涉图。我们使用了各种SAR传感器的干涉数据，包括低分辨率C波段ERS/ENVISAT、中分辨率L波段ALOS和高分辨率X波段COSMO-SkyMed，覆盖了意大利埃特纳火山地区。在模拟和实际干涉相位图像上，所提出的方法在条纹图案内都显示出显著的噪声降低，且没有任何有用信息的大量损失。
