CNN-based InSAR Coherence Classification
This work addresses noise reduction in InSAR processing for remote sensing applications, representing an incremental improvement through the application of CNNs to an existing domain.
The paper tackled the problem of noise contamination in InSAR imagery for ground movement estimation by using CNNs to improve coherence-based demarcation, resulting in reduced misclassifications in incoherent regions and demonstrated superiority over three established methods.
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.