Complex-Valued Convolutional Neural Networks in Interferometric Synthetic Aperture Radar and Their Teacher-Image Pollution Influence on the Performance

摘要

Complex-valued convolutional neural networks discover and/or adaptively classify local features in interferograms very effectively in interferometric synthetic aperture radar (InSAR). In this paper, we investigate the influence of label errors in teacher images on the classification performance. We find that performance is not affected so much from teacher-label errors as much as 10% or more. We also analyze the error characteristics experimentally.