  This work proposes an improved convolutional long short-term memory
(ConvLSTM) based architecture for selection of elite pixels (i.e., less noisy)
in time series interferometric synthetic aperture radar (TS-InSAR). Compared to
previous version, the model can process InSAR stacks of variable time steps and
select both persistent (PS) and distributed scatterers (DS). We trained the
model on ~20,000 training images (interferograms), each of size 100 by 100
pixels, extracted from InSAR time series interferograms containing both
artificial features (buildings and infrastructure) and objects of natural
environment (vegetation, forests, barren or agricultural land, water bodies).
Based on such categorization, we developed two deep learning models, primarily
focusing on urban and coastal sites. Training labels were generated from elite
pixel selection outputs generated from the wavelet-based InSAR (WabInSAR)
software developed by Shirzaei (2013) and improved in Lee and Shirzaei (2023).
With 4 urban and 7 coastal sites used for training and validation, the
predicted elite pixel selection maps reveal that the proposed models
efficiently learn from WabInSAR-generated labels, reaching a validation
accuracy of 94%. The models accurately discard pixels affected by geometric and
temporal decorrelation while selecting pixels corresponding to urban objects
and those with stable phase history unaffected by temporal and geometric
decorrelation. The density of pixels in urban areas is comparable to and higher
for coastal areas compared to WabInSAR outputs. With significantly reduced time
computation (order of minutes) and improved selection of elite pixels, the
proposed models can efficiently process long InSAR time series stacks and
generate rapid deformation maps.
