Enhanced Change Detection Performance Based on Deep Despeckling of Synthetic Aperture Radar Images

被引:2
作者
Ihmeida, Mohamed [1 ]
Shahzad, Muhammad [1 ]
机构
[1] Univ Reading, Dept Comp Sci, Reading, England
关键词
Change detection; convolutional neural network; despeckling noise; synthetic aperture radar; unsupervised learning; AUTOMATIC CHANGE DETECTION; UNSUPERVISED CHANGE DETECTION; CONVOLUTIONAL NETWORK; SAR; NOISE; MODEL; REGISTRATION; FUSION;
D O I
10.1109/ACCESS.2023.3307208
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Synthetic aperture radar (SAR) image change detection (CD) focuses on identifying changes between two images at different times for the same geographical region. Recently, several deep learning methods have been proposed for performing SAR based CD. However, speckle noise remains a major challenge for these methods. To address this, we propose a despeckling model (DM) that effectively suppresses speckle noise and enhances the performance of the existing CD methods. The proposed despeckling architecture is not only resilient to multi-temporal SAR acquired from one SAR imaging process (i.e., the same number of SAR images looks before and after the change) but also deals with any combination of single or multi-look images acquired prior and after the change. Moreover, as a second contribution, we have also proposed a loss function that effectively suppresses speckle noise, thereby improving the change detection accuracy. Both the despeckling model and the proposed tolerant noise loss function are evaluated extensively on three public real SAR datasets, achieving superior performance compared to existing state-of-the-art SAR CD methods in all datasets.
引用
收藏
页码:95734 / 95746
页数:13
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