SAR image speckle noise suppression algorithm based on background homogeneity and bilateral filtering

被引:0
|
作者
Ai J. [1 ,2 ]
Wang F. [1 ]
Yang X. [1 ]
Shi J. [3 ]
Liu F. [4 ]
机构
[1] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei
[2] Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei
[3] School of Software, Hefei University of Technology, Hefei
[4] Chinese Academy of space systems science and Engineering, Beijing
基金
中国国家自然科学基金;
关键词
Adaptive sample trimming; Adaptive window size; Homogeneity index of the reference window; Improve bilateral filtering; SAR image speckle noise reduction;
D O I
10.11834/jrs.20210212
中图分类号
学科分类号
摘要
As a kind of high-resolution imaging radar, Synthetic Aperture Radar (SAR) plays an important role in civil and military fields because it can realize all-weather and all-weather observation without the limitation of illumination and climate conditions. However, SAR also has limitations. For instance, the SAR image has many speckle noises, which is caused by the principle of coherent imaging that seriously affects the extraction and application of relevant information in the image. Therefore, to make better use of SAR image information, speckle noise reduction is a key step in SAR image processing. Among them, the bilateral filtering algorithm, which combines the geometric domain and the gray-scale domain information filter, is currently the best algorithm in the field of speckle noise removal. In this study, we take the bilateral filtering algorithm as the basic framework and then add corresponding improvement measures in view of the problems and shortcomings of the bilateral filtering algorithm, such as insufficient application of SAR image structure information and difficulty in effectively filtering out strong speckle noise. Finally, we propose an improved bilateral filtering algorithm based on background homogeneity (BH-IBF). This algorithm aims to effectively remove the speckle noise in the SAR image while retaining the real texture information of the image to the maximum extent. (1) The coefficient of variation is introduced into the weight kernel improvement of the bilateral filtering algorithm, compensating for the problem of the bilateral filter ignoring the structural information of the SAR image to a certain extent; (2) the sample truncation operation is introduced to filter the strong speckle noise in the background area to a certain extent, suppressing the influence of speckle noise, thus effectively solving the bilateral filtering. Strong speckle noise is difficult to filter out; (3) the half width of the background region for the homogeneous region can further improve the smoothness of the image.Taking the simulated and real SAR images intercepted from TerraSAR-X as the experimental objects, the comparison of the filtering effects and evaluation indexes of different filtering algorithms shows the improved results obtained by BH-IBF algorithm, indicating that the proposed algorithm achieves the research objective. The proposed BH-IBF has a better effect than the traditional filtering algorithm. BH-IBF can not only effectively suppress the speckle noise in the homogeneous region but also protect the edge texture information of the heterogeneous region better, that is, the algorithm can better guarantee the subsequent processing and application of SAR data. © 2021, Science Press. All right reserved.
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页码:1071 / 1084
页数:13
相关论文
共 40 条
  • [1] Ai J Q, Yang H, Yang X Z, Liu R M, Luo Q W, Zhang X H., Truncated-statistics-based bilateral filter for speckle reduction in synthetic aperture radar imagery, Journal of Applied Remote Sensing, 13, 2, (2019)
  • [2] Alonso-Gonzalez A, Lopez-Martinez C, Salembier P, Deng X P., Bilateral distance based filtering for polarimetric SAR data, Remote Sensing, 5, 11, pp. 5620-5641, (2013)
  • [3] Buades A, Coll B, Morel J M., Nonlocal image and movie denoising, International Journal of Computer Vision, 76, 2, pp. 123-139, (2008)
  • [4] Dabov K, Foi A, Katkovnik V, Egiazarian K., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, 16, 8, pp. 2080-2095, (2007)
  • [5] D'Hondt O, Guillaso S, Hellwich O., Iterative bilateral filtering of polarimetric SAR data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 3, pp. 1628-1639, (2013)
  • [6] Feng J D., Speckle Reduction Algorithms Based on Non-local Means for SAR Images, (2019)
  • [7] Frost V S, Stiles J A, Shanmugan K S, Holtzman J C., A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4, 2, pp. 157-166, (1982)
  • [8] Han J W, Kim J H, Cheon S H, Kim J O, Ko S J., A novel image interpolation method using the bilateral filter, IEEE Transactions on Consumer Electronics, 56, 1, pp. 175-181, (2010)
  • [9] Hu X., Despeckling and Segmentation Methods for Synthetic Aperture Radar Images, (2019)
  • [10] Jia K., Research on Nonlocal Mean Filtering Algorithm for PolSAR Image, (2019)