Following the 7.8 magnitude earthquake that struck Turkey and Syria on February 6, 2023, TomoSAR, an extensive software designed for SAR image processing, demonstrated its effectiveness in assessing land subsidence. It provided the initial three-dimensional displacement data, marking a significant milestone in this field. Notably, TomoSAR stands out as the first publicly accessible tool capable of jointly processing Persistent and Distributed Scatterers (https://github.com/DinhHoTongMinh/TomoSAR). Continual efforts are underway to elevate TomoSAR’s accessibility and performance. This involves integrating algorithms into a parallel version to facilitate enhanced performance and open avenues for complimentary scientific services at no cost.

Multitemporal SAR interferometry (MT-InSAR) is one of the most exploited phase-based InSAR techniques, capable of achieving millimeter per year accuracy [3] , [4] , [5] , [7] depending on the number of acquisitions and the spatial scale of the processing at which the displacement rate is measured. It helps to overcome the unwanted effects that may overwhelm the displacement patterns in standard InSAR, in particular the atmospheric delays that often dominate the individual interferograms [9] . Several techniques have been developed to handle the large stacks of SAR data. Some of these techniques, the distributed scatterers and the small baseline methods, employ spatial averaging to reduce the signal-to-noise ratio and to extend the spatial coverage of deformation measurements beyond the persistent scatterers present only in urbanized areas. This averaging in all cases involves a mixture of pixels that are more or less affected by changes of soil moisture and vegetation. It is a complex averaging process and therefore non-linear.

Unwrapping long temporal baseline interferograms in areas with abundant crop cover is essential to avoid phase bias in small baseline subset methods. The unwrapping method described here includes four steps, (1) flattening of wrapped interferograms, (2) noise reduction by multi-looking and filtering with quality assessment, (3) a region-growing quality-based unwrapping, (4) an iterative correction of unwrapping errors. We show the effectiveness of such a procedure on Sentinel-1 time series in NE France for generating time series on a 750km by 250km track.

Recently, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) radar interferometry technique has been implemented as an open-source TomoSAR package (https://github.com/DinhHoTongMinh/TomoSAR). TomoSAR offers state-of-the-art algorithms to capture your movement best. However, it is easy to make you crazy with memory requirements. Due to so many images to calculate, it says for only the covariance matrix with 200 images of 500x2000 size, 45 GB should be allocated for that. For a small computer, it can be a task impossible. For our cluster, the RAM is capacity up to TB. The good news is we can process free of charge for you under a scientific collaboration.

Measuring ground deformation is crucial in many fields of Earth sciences (eg. glaciers, landslides or tectonics) to understand the environmental forcing at work. In this work, we present the processing chain Multi-Pairwise Image Correlation for OPTical images (MPIC-OPT) to compute the ground displacement [1]. The chain is accessible on-line through different platforms (Geohazards Exploitation Platform -GEP- and For M@Ter webservice) allowing an easy access High-Performance Computing (HPC) infrastructures and currently ingest Sentinel-2 L1C data. The application of the method is presented for three use cases: a) to measure the co-seismic displacement of the Palu, Sulawesi (Indonesia) 2018 earthquake, b) to monitor ice velocity of the Khumbu glacier (Nepal), c) to monitor the ground displacement of the Slumgullion landslide (USA).

This paper presents a new method based on recent optimization technique to detect slow-moving landslides (1,2-norm is the most suitable norm for this detection problem, compared to pure ℓ1-norm or ℓ2-norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%.

Phase unwrapping is a very classical problem for the exploitation of SAR interferograms. Although many algorithms have been proposed to solve this problem, there are still many phase unwrapping errors on noisy interferograms or interferograms with sharp discontinuities. Therefore, we propose a simple algorithm to correct these errors based on the analysis of the borders of the incorrectly unwrapped regions on triplets of interferograms. This approach has been tested on two InSAR datasets in Lebanon and Turkey and has proven to efficiently decrease the number of unwrapping errors.

In this paper we present a method to summarize a satellite image time series. This summary is a small set of maps depicting salient phenomena occurring in the series over space and time. The approach is composed of a first step of extraction of spatiotemporal patterns, followed by an iterative ranking of these patterns using a swap randomization technique and a ranking based on a normalized mutual information measure. The best ranked patterns in the earliest iterations are in some sense the most informative and are used to build the summary. We present results showing that the approach is effective on both optical and radar data.

Synthetic aperture radar interferometry (InSAR) is limited by temporal decorrelation and topographic errors, which can result in unwrapping errors in partially incoherent and mountainous areas. In this paper, we present an algorithm to estimate and remove local digital elevation model (DEM) errors from a series of wrapped interferograms. The method is designed to be included in a small baseline subset (SBAS) approach for InSAR time series analysis of ground deformation in natural environment. It is easy to implement and can be applied to all pixels of a radar scene. The algorithm is applied to a series of wrapped interferograms computed from ENVISAT radar images acquired across the Himalayan mountain range. The DEM error correction performance is quantified by the reduction of the local phase dispersion and of the number of residues computed during the unwrapping procedure. It thus improves the automation of the spatial unwrapping step.

Synthetic Aperture Radar interferometry allows to measure spatio-temporal patterns of deformation. However this geodetic technique is limited by unwrapping difficulties linked with temporal decorrelation and topographic errors in partially incoherent and mountainous areas. This paper presents a new algorithm to correct and remove DEM errors in order to improve the phase unwrapping step. The method consists in a mix approach between Small Baseline and Permanent Scatterers strategy using a series of wrapped interferograms. First we develop our methodology and then we apply it to a series of wrapped ENVISAT interferograms on the Tibetan plateau.

An important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach.

This paper presents an original data mining approach for extracting pixel evolutions and sub-evolutions from Satellite Image Time Series. These patterns, called frequent grouped sequential patterns, represent the (sub-)evolutions of pixels over time, and have to satisfy two constraints: firstly to correspond to at least a given minimum surface and secondly to be shared by pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. Successful experiments on an optical and a radar SITS are presented.