Abstract
In this study, the impact of robust surface texture indices on the unsupervised morphological segmentation of an alpine basin is explored. Roughness indices calculated on a high resolution digital terrain model derived by means of airborne LiDAR (Light Detection and Ranging) are used as input features in the clustering procedure. The segmentation in textural classes is based on a fuzzy clustering approach, permitting the optimal selection of the number of clusters and fuzziness of the classification. A comparison is made between clusters derived from variogram-based indices and MAD–based (median absolute differences) roughness indices within a small alpine basin. As expected, with both approaches, the fuzzy clustering revealed a high fuzziness and a high degree of mixing between textural classes. However, the segmentation derived using MAD-based roughness indices shows relevant improvements; in particular, the spatial patterns of the MAD-based classification show less artifacts respect to the variogram-based one.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Rendiconti Online della Società Geologica Italiana
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
