Various studies have shown that image correlation calculated in the space domain outperforms frequency-based methods. However, such an approach usually requires great computational efforts, making it challenging to adopt for surveying fast moving processes like glaciers, particularly over wide areas. We present a local adaptive multiscale image matching algorithm (LAMMA), which repeatedly applies image correlation on grids of increasing spatial resolution and adapts the size of the interrogation area according to the local range of displacements. LAMMA allows reducing the number of calculi of several orders of magnitude and limits the occurrence of displacement outliers. We show an example of LAMMA application on Sentinel-2 images to measure glaciers flow of the Southern Patagonian Icefield, where LAMMA's runtime was comparable to that of frequency-based correlation. LAMMA's Matlab code is freely available on GitHub.
Fast local adaptive multiscale image matching algorithm for remote sensing image correlation / N. Dematteis, D. Giordan, B. Crippa, O. Monserrat. - In: COMPUTERS & GEOSCIENCES. - ISSN 0098-3004. - 159(2022 Feb), pp. 104988.1-104988.9. [10.1016/j.cageo.2021.104988]
|Titolo:||Fast local adaptive multiscale image matching algorithm for remote sensing image correlation|
|Parole Chiave:||Earth surface deformation; Fast correlation; Glacier flow; Image correlation; Matlab; Southern patagonian icefield|
|Settore Scientifico Disciplinare:||Settore ICAR/06 - Topografia e Cartografia|
|Data di pubblicazione:||feb-2022|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.cageo.2021.104988|
|Appare nelle tipologie:||01 - Articolo su periodico|