Synthetic Aperture Radar (SAR) Tomography (TomoSAR) allows extending the 2-D focusing capabilities of SAR to the elevation direction, orthogonal to the azimuth and range. The multi-dimensional extension (along the time) also enables the monitoring of possible scatterer displacements. A key aspect of TomoSAR is the identification, in the presence of noise, of multiple persistent scatterers interfering within the same 2-D (azimuth range plane) pixel. To this aim, the use of multi-look has been shown to provide tangible improvements in the detection of single and double interfering persistent scatterers at the expense of a minor spatial resolution loss. Depending on the system acquisition characteristics, this operation may require also the detection of multiple scatterers interfering at distances lower than the Rayleigh resolution (super-resolution). In this work we further investigated the use of multi-look in TomoSAR for the detection of multiple scatterers located also below the Rayleigh resolution. A solution relying on the Capon filtering was first analyzed, due to its improved capabilities in the separation of the responses of multiple scatterers and sidelobe suppression. Moreover, in the framework of the Generalized Likelihood Ratio Test (GLRT), the single-look support based detection strategy recently proposed in the literature was extended to the multi-look case. Experimental results of tests carried out on two datasets acquired by TerraSAR-X and COSMO-SkyMED sensors are provided to show the performances of the proposed solution as well as the effects of the baseline span of the dataset for the detection capabilities of interfering scatterers.

Synthetic Aperture Radar Interferometry (InSAR) with multiple passes is widely used to monitor deformations of the Earth surface. SqueeSAR and CAESAR (Component extrAction and sElection SAR) are two recently proposed solutions for performing an effective multi-acquisition multilook based filtering of interferograms to contrast the effects of phase noise for weak and/or decorrelating scatterers. Both methods can be used specifically at the detection stage in InSAR processing chains characterized by a two-stage/two-scale processing strategy, to increase the coverage of monitored points. In this work, we compare the detection performances of the SqueeSAR-based and CAESAR-based detectors. We address the evaluation of the performances on simulated data in the classical Gaussian scenario. Results of experiments on data acquired by the TSX and COSMO-SkyMED sensors are also included to discuss the advantages of one method over the other in real scenarios.

Due to the constant increase in Earth Observation (EO) data collections, the monitoring of land cover is facilitated by the temporal diversity of the satellite images datasets. Due to the capacity of Synthetic Aperture Radar (SAR) sensors to operate independently of sunlight and weather conditions, SAR image time series offer the possibility to form a dataset with almost regular temporal sampling. This paper aims at mining the SAR image time series for an analysis of target’s behavior from the perspective of both temporal evolution and coherence. The authors present a two-level analytical approach envisaging the assessment of global (related to perceivable structures on the ground) and local (related to changes occurred within a perceivable structure on the ground) evolution inside the scene. The Latent Dirichlet Allocation (LDA) model is implemented to identify the categories of evolution present in the analyzed scene, while the statistical and coherent proprieties of the dataset’s images are exploited in order to identify the structures with stable electromagnetic response, the so-called Persistent Scatterers (PS). A comparative study of the two algorithms’ classification results is conducted on ERS and Sentinel-1 data. At global scale, the results fit human perception, as most of the points which can be exploited for Persistent Scatterers Interferometry (PS-InSAR) are classified within the same class, referring to stable structures. At local scale, the LDA classification demands for an extended number of classes (defined through a perplexity-based analysis), enabling further differentiation inside the evolutional character of those stable structures. The comparison against the map of detected PS reveals which classes present higher temporal correlation, determining a stable evolutionary character, opening new perspectives for validation of both PS detection and SITS analysis algorithms.

Synthetic Aperture Radar (SAR) tomography can reconstruct the elevation profile of each pixel based on a set of co-registered complex images of a scene. Its main advantage over classical interferometric methods consists in the capability to improve the detection of single persistent scatterers as well as to enable the detection of multiple scatterers interfering within the same pixel. In this paper, three tomographic algorithms are compared and applied to a dataset of 32 images to generate the elevation map of dominant scatterers from a scene. Targets which present stable proprieties over time - Persistent Scatterers (PS) are then detected based on reflectivity functions reconstructed with Capon filtering.