论文标题
边缘保留CNN SAR伪装算法
Edge Preserving CNN SAR Despeckling Algorithm
论文作者
论文摘要
SAR Despeckling是地球观察的关键工具。 SAR图像的解释受到Speckle的损害，Speckle是一种与从照明场景到传感器的反向散射有关的乘法噪声。减少噪音是理解场景的关键任务。基于我们以前的解决方案KL-DNN的结果，在这项工作中，我们为培训卷积神经网络进行了恐惧的新成本函数。目的是控制边缘保存，并更好地过滤对KL-DNN非常具有挑战性的人造结构和城市地区。结果表明，在不均匀的领域中取得了很好的改善，可以使均匀的结果保持良好的效果。在论文中显示了模拟和真实数据的结果。
SAR despeckling is a key tool for Earth Observation. Interpretation of SAR images are impaired by speckle, a multiplicative noise related to interference of backscattering from the illuminated scene towards the sensor. Reducing the noise is a crucial task for the understanding of the scene. Based on the results of our previous solution KL-DNN, in this work we define a new cost function for training a convolutional neural network for despeckling. The aim is to control the edge preservation and to better filter manmade structures and urban areas that are very challenging for KL-DNN. The results show a very good improvement on the not homogeneous areas keeping the good results in the homogeneous ones. Result on both simulated and real data are shown in the paper.
