  In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (PS)
pixels are used to estimate geophysical parameters, essentially deformation.
Conventionally, PS pixels are selected on the basis of the estimated noise
present in the spatially uncorrelated phase component along with look-angle
error in a temporal interferometric stack. In this study, two deep learning
architectures, namely convolutional neural network for interferometric semantic
segmentation (CNN-ISS) and convolutional long short term memory network for
interferometric semantic segmentation (CLSTM-ISS), based on learning spatial
and spatio-temporal behaviour respectively, were proposed for selection of PS
pixels. These networks were trained to relate the interferometric phase history
to its classification into phase stable (PS) and phase unstable (non-PS)
measurement pixels using ~10,000 real world interferometric images of different
study sites containing man-made objects, forests, vegetation, uncropped land,
water bodies, and areas affected by lengthening, foreshortening, layover and
shadowing. The networks were trained using training labels obtained from the
Stanford method for Persistent Scatterer Interferometry (StaMPS) algorithm.
However, pixel selection results, when compared to a combination of R-index and
a classified image of the test dataset, reveal that CLSTM-ISS estimates
improved the classification of PS and non-PS pixels compared to those of StaMPS
and CNN-ISS. The predicted results show that CLSTM-ISS reached an accuracy of
93.50%, higher than that of CNN-ISS (89.21%). CLSTM-ISS also improved the
density of reliable PS pixels compared to StaMPS and CNN-ISS and outperformed
StaMPS and other conventional MT-InSAR methods in terms of computational
efficiency.
