A novel machine learning and deep learning semi-supervised approach for automatic detection of InSAR-based deformation hotspots

Abstract

Over the past two decades, Interferometric synthetic aperture radar (InSAR) has been invaluable for studying earth surface deformation and related effects. Deformation maps generated through multi-temporal InSAR processing methods are however difficult to interpret accurately by general individual users, decision-makers, and non-domain experts owing to the volume, variety, and velocity they are produced. This paper proposes a semi-supervised machine learning based information mining approach to simplify these deformation maps and detect hotspots by extracting prominent signals from time series deformation. The approach initially combines two machine learning based clustering methods named time series k-means (TSKM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to derive clusters with unique spatiotemporal deformation behavior, using time series deformation output generated from Wavelet-based InSAR (WabInSAR) method. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop two deep learning models, one using long short term memory (LSTM) networks alone and another using a combination of LSTM and single-layer perceptron for supervised training. The developed LSTM and LSTM + Perceptron models efficiently learn from the cluster labels, reaching an accuracy of 97.3 %. Further, the deep learning models significantly reduce the computational time from orders of days (∼5) to hours (∼2) while training and from hours to minutes during prediction. We evaluate the developed approach over Los Angeles, a highly challenging area affected by umpteen deformation events that are challenging to categorize. The outcome of the proposed approach produces hotspots of deforming areas in Los Angeles, providing a generalized and more precise picture of events, much appreciable to non-domain experts. The approach can augment any of the multi-temporal InSAR processing chains and is applicable to different deformation prone sites, aiding in derivation of deformation hotspots from time series deformation maps.