A mean based algorithm for the multi-temporal SAR image filtering

被引:0
|
作者
Coltuc, D [1 ]
Becker, JM [1 ]
Radescu, R [1 ]
机构
[1] Politehn Univ Bucharest, Bucharest, Romania
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Any despeckling method for SAR images is a trade-off between speckle reduction level and preservation of image's resolution. The Time-Space Filter (TSF) proposed in III, despeckles the multi-temporal SAR images while preserving the radar reflectivity, the textures and the edges' contrast. The price paid for this features' integrity is a non-uniform despeckling: the speckle is reduced only in the homogenous areas, whereas the edges' vicinities and the textured areas remain noisy. In this paper, we propose a new version of the Time-Space Filter, the Mean based Time-Space Filter (M-TSF), which reduces everywhere the speckle, disregarding image's content. The residual noise in the images filtered by M-TSF remains multiplicative and stationary. This latter property makes M-TSF appropriate for pre-treatment in automatic edge detection. A comparison between M-TSF and Lee's filter for multipolarization SAR images [2] leads to an interesting conclusion: M-TSF is a frequential version of the Lee's filter.
引用
收藏
页码:1798 / 1800
页数:3
相关论文
共 50 条
  • [1] Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform
    Bu, Lijing
    Zhang, Jiayu
    Zhang, Zhengpeng
    Yang, Yin
    Deng, Mingjun
    [J]. SENSORS, 2023, 23 (21)
  • [2] Use of Multi-Temporal SAR Non-Local Mean Filtering Operations for Change Detection Analyses
    Pepe, Antonio
    [J]. 2022 IEEE 21ST MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (IEEE MELECON 2022), 2022, : 616 - 620
  • [3] A new method for multi-temporal SAR image-based change detection
    Zhang, Juntuan
    Huang, Shiqi
    Zhu, Guangliang
    Lin, Jun
    [J]. PROCEEDINGS OF THE 8TH INTERNATIONAL SYMPOSIUM ON SPATIAL ACCURACY ASSESSMENT IN NATURAL RESOURCES AND ENVIRONMENTAL SCIENCES, VOL II: ACCURACY IN GEOMATICS, 2008, : 298 - 304
  • [4] Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack
    Lin, Keng-Fan
    Perissin, Daniele
    [J]. REMOTE SENSING, 2018, 10 (07):
  • [5] A cellular automata-based filtering approach to multi-temporal image denoising
    Priego, Blanca
    Prieto, Abraham
    Duro, Richard J.
    Chanussot, Jocelyn
    [J]. EXPERT SYSTEMS, 2018, 35 (02)
  • [6] FULLY CONVOLUTIONAL NETWORKS FOR MULTI-TEMPORAL SAR IMAGE CLASSIFICATION
    Mullissa, Adugna G.
    Persello, Claudio
    Tolpekin, Valentyn
    [J]. IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 6635 - 6638
  • [7] Development of an algorithm for the knowledge-based classification of multi-temporal SAR-images
    Habermeyer, M
    Schmullius, CC
    [J]. RADAR PROCESSING, TECHNOLOGY, AND APPLICATIONS II, 1997, 3161 : 148 - 158
  • [8] Oil Platform Investigation by Multi-temporal SAR Remote Sensing Image
    Chen, Peng
    Wang, Juan
    Li, Donglin
    [J]. SAR IMAGE ANALYSIS, MODELING, AND TECHNIQUES XI, 2011, 8179
  • [9] MULTI-TEMPORAL SUPERPIXEL GENERATION FOR HIGH RESOLUTION SAR IMAGE ANALYSIS
    Hu, Hao
    Liu, Bin
    Zhang, Zenghui
    Liu, Xingzhao
    Yu, Wenxian
    [J]. 2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, : 1686 - 1689
  • [10] Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning
    Wang, Caiqiong
    Zhao, Lei
    Zhang, Wangfei
    Mu, Xiyun
    Li, Shitao
    [J]. PEERJ, 2022, 10