论文标题
基于CNN的INAR连贯分类
CNN-based InSAR Coherence Classification
论文作者
论文摘要
基于微波反射的地面目标反射的干涉合成孔径（INSAR）图像在遥感中越来越重要，以进行地面运动估计。但是，这些反射被噪声污染，这会扭曲信号的包裹相。基于污染程度（“相干”）的图像区域的分界是Insar处理管道的重要组成部分。我们将卷积神经网络（CNN）介绍到此问题域中，并通过智能预处理培训数据来改善基于相干的分界和减少完全不相互关联区域的错误分类的有效性。定量和定性比较证明了所提出的方法优于三种已建立的方法。
Interferometric Synthetic Aperture Radar (InSAR) imagery based on microwaves reflected off ground targets is becoming increasingly important in remote sensing for ground movement estimation. However, the reflections are contaminated by noise, which distorts the signal's wrapped phase. Demarcation of image regions based on degree of contamination ("coherence") is an important component of the InSAR processing pipeline. We introduce Convolutional Neural Networks (CNNs) to this problem domain and show their effectiveness in improving coherence-based demarcation and reducing misclassifications in completely incoherent regions through intelligent preprocessing of training data. Quantitative and qualitative comparisons prove superiority of proposed method over three established methods.
