Multi-temporal satellite remote sensing images registration in mountainous forestland based on robust principal component analysis

Abstract

The mountainous woodland covering with dense vegetation has complex terrain deformation, poor surface stability and rare obvious markers, which brings challenges to the accurate registration of multi-temporal remote sensing images. Viewing multi-temporal satellite image sequences as a whole matrix, we conduct RPCA matrix decomposition to generate a low-rank matrix and a sparse matrix, where the column of low rank matrix can be considered as the stable surface image. Referring to this, the original image registration is operated. It solves the difficulty to distinguish the real change of scenery and the distortion of remote sensing image in the case of unstable features and lack of obvious markers. Based on the feature matching method and local coordinate transformation and resampling model, the multi-temporal images are respectively registered with their stable surface images, and finally realize the batch accurate registration of multi-temporal satellite images of mountain forestland.