  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.
