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The recent availability of wide-bandwidth, high-frequency, high-resolution SAR data is contributing to improved monitoring capabilities of spaceborne remote sensing instruments. In particular, the new COSMO/SkyMed (CSK) and TerraSAR- X (TSX) X-band sensors allow better performances in multitemporal DInSAR and PSI applications than legacy C-band sensors such as ENVISAT ASAR, with respect to both target detection and terrain displacement monitoring capabilities. In this paper we investigate about the possibility of achieving performances of PSI displacement detection comparable to those of C-band sensors, by use of reduced numbers of high-resolution X-band acquisitions. To this end, we develop a simple model for phase and displacement rate measurement accuracies taking into account both target characteristics and sensors acquisition schedule. The model predicts that the generally better resolution and repeat-time characteristics of new-generation X-band sensors allow reaching accuracies comparable to C-band data with a significantly smaller number of X-band acquisitions, provided that the total time span of the acquisitions is large enough. This allows in principle to contain the costs of monitoring campaigns, by using less scenes. Indications are more variable in the case of short-time acquisition schedules, such as those involved in the generation of so-called "rush products" for emergency applications. In this case, the higher uncertainty given by shorter total time spans lowers X-band performances to levels mostly comparable to those of the legacy medium-resolution C-band sensors, so that no significant gain in image number budget are foreseen. These theoretical results are confirmed by comparison of three PSI datasets, acquired by ENVISAT ASAR, CSK and TSX sensors over Assisi (central Italy) and Venice.

The analysis of multi-temporal SAR data-sets encountered large interest in the remote sensing community during the past few years.The main effort goes toward the extraction of ground displacements signals by means of differential interferometric techniques.In this opera- tional framework an important processing step concerns the estimation and subtraction of signals due to atmospheric artifacts and processing errors.In the present work we apply the technique of Blind Source Separation (BSS) by using the algorithm of Independent Component Analysis (ICA) to Permanent Scatterer processing in order to perform the separation of different signal components.Preliminary investigations are carried out both on simulated and real ERS-1/2 data and results are reported and commented. I. INTRODUCTION The permanent scatterers (PS) technique (1) allows the analysis of the phase information over single objects and thus typically on man-made structures, characterized by a high temporal phase stability. Where such object are present, even when surrounded by low-coherence condi- tions, a non-conventional spatio-temporal phase analysis can be performed, together with an accurate atmospheric phase screen estimation and removal. This new technique, although very promising as an operational tool for the analysis of long series of SAR images, represents still a challenge in termsof computational burden and exhibits modelling aspects not yet fully understood. The main problemsencountered when dealing with data on moun- tainous, un-urbanised areas involve the steep topography causing considerable DEM errors (∆h(x, y)), increased atmospheric effects compared to flat areas, and increased geometric distortions; also, the scarcity of man-made structures lowers considerably the number of detectable stable points, further complicating the processing (5). Many as pectsof the complicationsintroduced in the