Change detection from high-resolution airborne laser scans using penalized composite likelihood screening

The study of elevation change using pre- and post-event raster grids obtained by airborne laser scans is central to many scientific areas, including geomorphology, landscape science and environmental science. Separating real elevation change from noise is a nontrivial task due to complex sources of measurement uncertainty causing errors to be spatially variable. For high-resolution grids, an additional hurdle is the excessive computational effort required by common statistical models for the spatial error. This paper introduces a screening methodology for change detection in high-resolution elevation difference grids using a composite likelihood regularization framework. We propose to model the spatial error by a sparse spatial autoregressive conditional heteroscedasticity process and obtain simultaneous detection of change location and change size estimation by maximizing a penalized composite likelihood objective. The methodology is implemented through a fast coordinate-wise maximization algorithm with computational complexity growing linearly with the grid size. Numerical studies on simulated and real data show satisfactory accuracy for both change detection and parameter estimation.