Repeat-pass acquisitions with coherent Synthetic Aperture Radar (SAR) systems, preserving both phase and amplitude, are more readily available than ever (Bruzzone, 2016). Phase measurements from SAR systems have seen widespread use in the Interferometric Synthetic Aperture (InSAR) technique to measure deformations and elevations since the late 1980’s (Hanssen, 2002). Since the late 1990’s an increase in radar-based change detection is observed, mainly relying on amplitude measurements (Ajadi et al., 2016; Dekker, 1998). The unpredictive multiplicative noise-like speckle, inherent to coherent SAR,makes change detection in SAR imagery difficult (Bamler, 2015). However, the advantages in the all-weather mapping capabilities and object penetrating properties of SAR make it a suitable remote sensing technique for certain applications, such as natural disaster damage assessment (Bruzzone and Prieto, 2000). Broadly speaking, change detection in SAR-based images usually consists of applying an operator on two spatially filtered SAR images to create a difference image (DI), which is then analysed for change points by thresholding and/or clustering (e.g. Alphonse and Biju, 2015). However, such an approach completely neglects the long-term stability of a pixel. When taking the temporal evolution of a pixel into account, the steep increase in data volume puts an emphasis on finding an optimal (’best practice’) approach to the multitemporal change point detection problem. Here it is shown that change point detection methods that properly take the temporal evolution of a pixel into account can provide good segmentation results in multi-temporal SAR data stacks, even in unfiltered stacks that preserve the complete spatial resolution and without considering the spatial context in a pixel’s direct neighbourhood. Moreover, it is found thatmore sophisticated change point detection algorithms don’t
necessarily yield superior segmentation results for various discontinuity functions. This means algorithm selection has to be application driven. The results demonstrate that the suitability of algebraic methods in heterogeneous areas is limited, whereas proper time series analysis yields fairly consistent results over different land covers within the same image. Often, little effort is spend on finding an optimal approach; neglecting data selection and storage, a sensitivity analysis and/or the post-processing analysis procedure, all of which are shown or known to increase the success rate, efficiency and understanding of the segmentation results. It is anticipated that change point detection in SAR imagery will shift away from the classical bi-temporal methodology and multi temporal approaches will become the norm, be it by decomposing multi-temporal stacks or time series analysis.