PSI Clustering for the Assessment of Underground Infrastructure Deterioration

Abstract

Remote sensing images find application in several different domains, such as land cover or land usage observation, environmental monitoring, and urbanization. This latter field has recently witnessed an interesting development with the use of remote sensing for infrastructural monitoring. In this work, we present an analysis of Sentinel-1 images, which were used to monitor the Italian provinces of Bologna and Modena located at the Emilia Region Apennines foothill. The goal of this study was the development of a machine learning-based detection system to monitor the deterioration of public aqueduct infrastructures based on Persistent Scatterer Interferometry (PSI). We evaluated the land deformation over a temporal range of five years; these series feed a k-means clustering algorithm to separate the pixels of the region according to different deformation patterns. Furthermore, we defined the critical areas as those areas where different patterns collided or overlapped. The proposed approach provides an informative tool for the structural health monitoring of underground infrastructures.

A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence

“…which is the sum of the squared Euclidean distances between the objects y i and their centroids. It has to be minimized with respect to centroids coordinates, while the number of clusters represents a fixed parameter [22]. Once cluster memberships in the embedded space are defined, they are exploited in the original feature space, where the associated centroids coordinates define a new covariance matrix, whose PCA represents the starting of the next iterative embedding.…”

“…In particular, the decision tree nature of molecular subtypes allows us to improve explainability [10]. To evaluate the robustness of the cluster analysis, several metrics were investigated, such as total within sum of squares, average silhouette width, Dunn index and adjusted Rand index [22,24,25] (Appendix C).…”

“…The range of this quantity is [-1,1], with the lower bound corresponding to high dissimilarity with patients within the same cluster, while the upper one expresses high similarity with remaining members [22]. We will consider the mean value evaluated over all patients to define the average silhouette width, whose maximization targets the selection of an optimal number of clusters.…”

“…equal to a normalized difference of the Rand Index and its expected value [24]. This metric has range [−1,1] with 1 corresponding to perfect mutual agreement, −1 corresponding to perfect disagreement and 0 that correspond to random agreement between the two partitions [22], so measuring the mutual agreement of labels pairs from two clusterings.…”

Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data

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