  In this paper, we scrutinize the effectiveness of various clustering
techniques, investigating their applicability in Cultural Heritage monitoring
applications. In the context of this paper, we detect the level of
decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes
utilizing hyperspectral images. A total of 6 different clustering approaches
have been evaluated over a set of 14 different orthorectified hyperspectral
images. Experimental setup in this study involves K-means, Spectral, Meanshift,
DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate
its performance by the use of performance metrics such as Calinski-Harabasz,
Davies-Bouldin indexes and Silhouette value. In this approach, we evaluate the
outcomes of the clustering methods by comparing them with a set of annotated
images which denotes the ground truth regarding the decomposition and/or
corrosion area of the original images. The results depict that a few clustering
techniques applied on the given dataset succeeded decent accuracy, precision,
recall and f1 scores. Eventually, it was observed that the deterioration was
detected quite accurately.
