This study presents and evaluates an algorithm which uses the least squares image matching (LSM) with spatially adaptive and high-order geometric models to estimate horizontal surface displacements of slow-moving landslides from repeat optical images. Pairs of high resolution optical images over a rockglacier creep and a slow-moving landslide are orthorectified and co-registered. Image matching is applied first using the conventional normalised cross-correlation (NCC), then using the LSM algorithm with image-wide single geometric models, and finally using the LSM with spatially adaptive geometric models. The spatially adaptive algorithm operates in such a way that for each template the model that produces the lowest sum of square of intensity difference is considered the best fitting model. Affine, projective and second-degree polynomial geometric models are included. The algorithms are evaluated in reference to the NCC algorithm based on the signal-to-noise ratio of reconstructing the reference image from the search image. © Springer-Verlag Berlin Heidelberg 2013.
CITATION STYLE
Debella-Gilo, M., & Kääb, A. (2013). Monitoring slow-moving landslides using spatially adaptive least squares image matching. In Landslide Science and Practice: Early Warning, Instrumentation and Monitoring (Vol. 2, pp. 301–307). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-642-31445-2_39
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