论文标题
基于CNN的INAR DENOISING和COOLERESS指标
CNN-based InSAR Denoising and Coherence Metric
论文作者
论文摘要
基于微波反射的地面目标，用于估计地面运动的干涉合成孔径（INSAR）图像在遥感中的重要性越来越重要。但是，噪声会破坏在卫星处收到的微波反射，并污染信号包裹的阶段。我们将卷积神经网络（CNN）介绍到此问题领域，并显示自动编码器CNN体系结构在没有清洁地面真相图像的情况下学习内部图像降低过滤器的有效性，并通过智能预处理训练数据来减少估计的连贯性。我们将结果与四种既定方法进行比较，以说明提出的方法的优越性。
Interferometric Synthetic Aperture Radar (InSAR) imagery for estimating ground movement, based on microwaves reflected off ground targets is gaining increasing importance in remote sensing. However, noise corrupts microwave reflections received at satellite and contaminates the signal's wrapped phase. We introduce Convolutional Neural Networks (CNNs) to this problem domain and show the effectiveness of autoencoder CNN architectures to learn InSAR image denoising filters in the absence of clean ground truth images, and for artefact reduction in estimated coherence through intelligent preprocessing of training data. We compare our results with four established methods to illustrate superiority of proposed method.
