Multi-swath SAR interferometry is a powerful tool for assessing sub-wavelength changes over large-scale areas. The azimuth variation of the line of sight (LOS) induces phase jumps between adjacent bursts in the interferograms which contain useful information about the motion. In this work, we present a multitask convolutional neural network that simultaneously decouples the interferometric phase due to displacements in the LOS direction from that due to displacements in the along-track direction, and predicts a proxy for the alongtrack displacement. We show results using a single pair of Sentinel-1 acquisitions over the inland region of Greenland, where glacier flows occur in the winter season within the revisit time

The scanning synthetic aperture radar (ScanSAR) and Terrain Observation by Progressive Scans (TOPSs) burst acquisition modes are nowadays among the most widely used in synthetic aperture radar (SAR) satellite missions. Both allow for increased coverage at the expense of azimuth resolution. However, the intermittent nature of the burst acquisition results in an increased sensitivity toward burst edges to displacements in the along-track (AT) dimension. In the presence of azimuth motion in the scene, phase jumps between bursts occur. In this contribution, this increased sensitivity is considered as an opportunity to obtain information on the North-South displacement, in which current SAR systems are less sensitive due to their quasi-polar orbits. Specifically, we suggest the usage of a multitask learning (MTL) architecture trained in a supervised fashion to separate the phase contribution due to displacements in the zero-Doppler (ZD) direction from AT displacements and to further provide a first rough estimation for the along-track displacement. Through an ad hoc network architecture and loss functions, we inject information about the interferometric SAR system model into the learning process, following a machine learning approach. We apply our method to the estimation of inland glacier flow from Sentinel-1 interferometric wide (IW)-swath data. We show that we are able to estimate, with an excellent performance, AT surface displacements of a few centimeters to several tens of centimeters, providing an improvement in accuracy compared with speckle tracking, and in coverage compared with techniques that exploit the burst-overlap differential phase.

This paper presents our new SAR processing framework known as PRISM (Processor for Interferometric SAR Missions). This flexible approach allows the efficient and accurate processing of SAR data independent of the sensor and the acquisition mode. The two main PRISM components (the focusing and the interferometric chains) are described in this paper together with the philosophy of the software architecture. Experimental results are presented and discussed based on the impulse response function analysis of simulated data as well as the focusing and interferometric results using real TerraSAR-X data.

Object recognition in synthetic aperture radar images is a well studied topic that has gained a significant amount of attention within the last decades. Modern approaches are based on machine learning, i.e. deep learning, and often show excellent performance. What is so far missing in the literature is a study dedicated to the generalization capabilities of object recognition approaches, i.e. how well a given system can be transferred to new and previously unseen data. In this paper, the proposed recognition model is trained and tested on a unique dataset of 25 high-resolution TerraSAR-X images (X-band), acquired over four different airports in Staring Spotlight mode. We show how classification performance changes for different application scenarios which require different training and evaluation setups.

The TanDEM-X Synthetic Aperture Radar (SAR) system allows for the recording of the bistatic interferometric coherence, which adds additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which influences the interferometric coherence, has been proved to be a reliable indicator of vegetated areas and was exploited in [1] to generate the global TanDEM-X Forest/Non-Forest Map, based on a supervised clustering algorithm. In this work, we investigate ad-hoc training strategies to extent the Convolutional Neural Network (CNN) presented in [2] for mapping forests and monitoring the extend of the Amazonas using TanDEM-X. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the clustering approach, with an f-score increase of 0.13, using as reference a forest map of 2010 based on Landsat data. The improvement in the forest classification makes it possible to skip the weighted mosaicking of overlapping images used in the clustering approach for achieving a good final accuracy. In this way, we were able to generate three time-tagged mosaics over the Amazon rainforest, by utilizing the nominal TanDEM-X acquisitions between 2011 and 2017. In the final paper, we will present more consolidated results, including the validation and comparison of the generated mosaics, as well as change detection investigations, aimed at showing the capabilities of Deep Learning approaches for forest mapping and monitoring with bistatic TanDEM-X images.

Decorrelation phenomena are always present in synthetic aperture radar interferometry (InSAR). While this implies a certain level of signal degradation, decorrelation is also a characteristic of the type of imaged target itself and can, therefore, be seen as a source of information. In this letter, we investigate InSAR decorrelation effects at the X-band by fitting volume and temporal decorrelation trends using the unique combination of data provided by the TanDEM-X (TDX) and PAZ spaceborne missions. The innovative use of this constellation allows for the acquisition of both single- and repeat-pass data at short revisit times. The concurrent availability of simultaneous acquisitions and the fine temporal resolution makes this constellation the ideal observation scenario for the study of decorrelation phenomena. Overall, we analyze five test sites, characterized by the presence of different land cover classes, and for each of them, we provide volume and temporal decorrelation fitting parameters. The performed analysis gives a first insight on the potential of combining bistatic and repeat-pass InSAR acquisitions also in view of future spaceborne constellations, which could benefit from the TDX/PAZ experience.

Decorrelation phenomena are always present in Synthetic Aperture Radar Interferometry (InSAR). While this implies a certain level of signal degradation, the decorrelation is also a characteristic of the type of imaged target itself and can therefore be seen as a source of information. Correctly accounting for the type and amount of decorrelation is crucial when using InSAR systems for land classification purposes. In this paper we aim at modeling InSAR decorrelation effects at X-band for several land cover classes. In particular we model the volume and temporal decorrelation, by exploiting TanDEM-X and PAZ joint time-series. The uniqueness of the combined use of these two missions is the availability of simultaneous bistatic as well as short revisit time repeat-pass acquisitions, making it the ideal observation scenario for the study of decorrelation phenomena. The paper shows the preliminary results of the analysis on the city of Madrid (Spain) and for two land cover classes.

The EC/ESA Copernicus program provides a long-term data base that is a unique opportunity for the constant monitoring of the dynamic processes of our Planet. The observation of the forest coverage is of primary importance for the study of the carbon cycle and plays a fundamental role for the management of Earth's natural resources. In this paper we present a strategy to map forested areas by exploiting interferometric Sentinel-1 acquisitions. We observe the evolution in time of the temporal decorrelation of Sentinel-1 stacks and provide a processing and classification framework. We show results over the Amazon rainforest, in particular over the Brazilian Rondonia state, where intensive deforestation phenomena take place.

In this paper, we present a novel approach to monitor the evolution of deforested areas in the Amazon rainforest, by combining Sentinel-1 and TanDEM-X SAR data. The idea is firstly to exploit the large coverage and short revisit time provided by the constellation of Sentinel-1 satellites in order to cover the entire arch of deforestation about ones per month. The goal is here to discriminate forest/non- forest by exploiting C-band backscatter signatures in dual polarization together with the behavior of the interferometric coherence in time, in order to identify the so-called deforestation hotspots: local areas characterized by a significant amount of on-going deforestation activities. Secondly, high-resolution time series of bistatic TanDEM-X data can be acquired over these hot spots with a repeat-cycle of 11 days, and used to track fast changes at small scales, aiming at identifying specific on-going deforestation activities. In the final paper, we intent to present more consolidated results, supported by a large scale acquisition scenario.

TanDEM-X forms together with TerraSAR-X the first single-pass polarimetric interferometer in space. This allows for the first time the acquisition and analysis of Single-, Dual-, and Quad-Pol-InSAR data without the disturbing effect of temporal decorrelation globally. For this reason, the exploration of TanDEM-X data for forestry is constantly increasing especially concerning forest height estimation, biomass classification and structure characterization. This paper reports the results of recent experiments aimed at investigating the potentials of TanDEM-X in characterizing quantitatively the spatial variability of the canopy top and phase center height, which is a proxy to horizontal structure. It is shown that such characterization can allow to differentiate among e.g. different successional and / disturbance stages in tropical forests.

Research and application is spreading of techniques based on coherent combination of SLC (amplitude and phase) SAR data to extract rich information even on complex observed scenes, fully exploiting existing SAR data archives, and new tandem satellites. Among such techniques, Tomo-SAR stems from multibaseline interferometry to achieve full-3D imaging through elevation beamforming. The Tomo concept has been integrated with differential interferometry, producing the new Differential Tomography (Diff-Tomo) processing mode, that allows “opening” the SAR cells in complex non-stationary scenes, resolving multiple heights and deformation velocities of layover (double) scatterers. In this paper, recent experiments carried out at University of Pisa and at the RaSS Nat. Lab. of the Italian National Consortium for Telecommunications (CNIT), and latest advances are presented of Diff-Tomo techniques for the efficient reconstruction and monitoring of complex urban/infrastructure deformating scenarios with new generation VHR SAR data. In particular, a new 4D Diff-Tomo model-based method with single-look light-burden superresolution capabilities is presented, and validated with COSMO-SkyMed (CSK) data. Also, first investigations of triple scatter detection, and of eigenvalue-based pre-detection are carried out with the CSK data. Finally, a new “5D” Diff-Tomo model-based method for non-uniform motion (thermal dilation) monitoring at superresolution is introduced, and tested. This ensemble of results is the first extensive demonstration of multidimensional superresolution tomographic methodologies for CSK applications.