Detection of Partially Structural Collapse Using Long‐Term Small Displacement Data from Satellite Images

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long‐term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR‐based SHM. Conversely, the major challenge of the long‐term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis‐squared distance. The first method presented in this work develops an artificial neural network‐based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher– student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long‐term displacement samples extracted from a few SAR images of TerraSAR‐X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR‐based SHM applications.