Pattern recognition approaches for the detection of alteration and degradation phenomena in hyperspectral and UAV multispectral imagery: the case study of a historical masonry water bridge

Abstract. Historical masonry hydraulic infrastructures are affected by complex degradation processes, including vegetation growth, moisture-related anomalies, and salt efflorescence, whose detection requires non-invasive, repeatable, and scalable diagnostic approaches. This study proposes a multi-scale workflow for detecting and classifying degradation phenomena affecting the Cavour Canal water bridge, a nineteenth-century masonry structure in northern Italy. The methodology combines UAV-based multispectral orthophotos and close-range hyperspectral imagery within a common Object-Based Image Analysis (OBIA) framework. The multispectral workflow was designed for façade-scale screening, whereas the hyperspectral workflow was used to support the detailed characterisation of selected sectors through detailed spectral characterisation. Multiple supervised classifiers, including Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Decision Tree (DT), Random Trees (RT), and Naïve Bayes (NB), were tested on both datasets. The results show that the multispectral workflow is effective for identifying vegetation and broad water-related anomalies, with kNN providing the best overall performance, while the hyperspectral workflow improves the discrimination of subtle surface alterations, particularly efflorescence, with SVM yielding the most stable results across the tested configurations. Overall, the proposed methodology demonstrates the value of a hierarchical multispectral and hyperspectral workflow for non-invasive degradation mapping of historical masonry hydraulic infrastructures.