Split-Band SAR and Split Band InSAR principle and applications

Most recent SAR sensors use wide band signals to achieve metric range resolution. One can also take advantage of wide band to split it into sub-bands and generate several lower-resolution images, centered on slightly different frequencies, from a single acquisition (Bovenga & al. 2014). This process, named Multi Chromatic Analysis (MCA) corresponds to performing a spectral analysis of SAR images. From this spectral analysis, three potential applications are shown.
First, this splitting allows performing a spectral analysis of observed scatterers. Spectral coherence is derived by computing the coherence between sub-images issued from a single SAR acquisition. It was shown that in the presence of a random distribution of surface scatterers, spectral coherence is proportional to sub-band intersection of sub-images. This model is fully verified when observing spectral coherence on open seas areas. If the scatterers distribution departs from this distribution, like for manmade structures, spectral coherence may be preserved to a certain degree and allows discriminating vessels from see clutter even in case of high sea state. This property can be used to perform vessel detection (Derauw & al., 2010).
Second, Split-Band SAR interferometry (SBInSAR) is also based on this spectral analysis performed on each image of an InSAR pair, yielding a stack of sub-band interferograms. Scatterers keeping a coherent behaviour in each sub- band interferogram show a phase that varies linearly with the carrier frequency, the slope being proportional to the absolute optical path difference. This potentially solves the problems of phase unwrapping on a pixel-per-pixel basis (Libert & al.).
Third, unwrapping classically two sub-band interferograms allows getting two phases of a same scene and same ionospheric components. Since these two components behave differently with frequency, SBInSAR allows discriminating both and remove the ionospheric artifacts if presents (Gomba & al. 2016, Furuya & al. 2016).