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It is crucial to identify potential natural patterns and employ them for measuring displacements of large structures. However, detecting and matching unique features over a natural pattern is challenging due to the random order, intensity and movement of the pixels. This study presents a feature-based methodology that employs the KAZE feature detection and descriptor algorithm due to its superior capability in dealing with complex natural features experiencing complicated scale-space changes. The developed methodology identifies a natural pattern in the reference image and determines the strongest interest point (SIP) with the help of the KAZE algorithm. This reference key point, i.e. the SIP, is used to determine the corresponding matches in the other images to obtain the displacements and the natural frequencies. The work conducts simulation and indoor experiments for validation and applies the developed …