Deep Learning Improves Point Density in PS-InSAR Data Toward Finer-Scale Land Surface Displacement Detection

Abstract

The permanent scatterer interferometric aperture radar (PS-InSAR) technique is used to measure and monitor displacements of the Earth’s surface over time. While the approach is promising for large-scale deformation, the density of the received PS points is insufficient for localized deformation analysis. In this first work, we aim to improve the technique by increasing the point density of high-precision deformation monitoring in PS-InSAR data by developing a convolutional long short-term memory (ConvLSTM) model that predicts PS points on different land covers, such as forest, urban, natural, water, and combinations among them. The proposed architecture, PS-ConvLSTM, was trained on a temporary dataset with interferograms to classify stable and unstable PS pixels from over 200,000 site images obtained from the city of Barcelona, Spain. The result showed that the trained PS-ConvLSTM model is highly compatible with the method currently used, which requires a large manual effort by an expert (accuracy: 99%). In addition, the proposed approach increased the point density by 15%, indicating that ConvLSTM is a promising approach for increasing the point density in PS-InSAR data and thus improving localized deformation analysis.

Keywords

• Deep learning
• recurrent neural network
• long short-term memory
• point density
• persistent scatter
• InSAR process