Acquisition Geometry Research Articles

An Image Formation Algorithm for Bistatic SAR Using GNSS Signal With Improved Range Resolution

Passive synthetic aperture radar (SAR) using Global Navigation Satellite System (GNSS) signals as the illuminators of opportunity has a relative coarse range resolution due to the narrow bandwidth of the navigation signals and the bi-static acquisition geometry, limiting its application for high-resolution requirement. In this paper, an image formation algorithm for range resolution improvement is investigated. A modified second order differentiation based method is applied on the range compressed signal of reference channel and surveillance channel, improving range resolution and keeping side-lobes at a lower level simultaneously, and the phase of processed signal is preserved by phase multiplication compensation for the following azimuth compression processing. To validate the effectiveness and benefits of the proposed algorithm in terms of range resolution and targets side-lobes, point targets simulation under different situations and real field experiment with strong scatters are conducted and discussed. Compared with the traditional GNSS-based passive SAR imaging algorithm, the results of the proposed method show that the imaging performance can be improved for better range resolution and lower side-lobes, benefitting for better distinguishing nearby targets and recognizing weak targets.

ACYOTB Plugin: Tool for Accurate Orthorectification in Open-Source Environments

High-resolution satellite images must undergo a geometric rectification process in order to be used for metrical purposes. This operation, called orthorectification, is necessary because of deformations mainly due to camera distortions and acquisition geometry. To correctly orthorectify an image, it is necessary to accurately reconstruct the photogrammetric-acquisition characteristics and the image position with respect to a reference system connected to the ground. This operation, called orientation, can be done using various mathematical models such as rigorous, rational polynomial function (RPF), and rational polynomial coefficient, or, according to some authors, rapid positioning coefficient (RPC) models. Orientation and orthorectification are usually performed within specific commercial software, but in QGIS, these complex operations can be performed using the open libraries of the Orfeo Tool Box (OTB). Unfortunately, instructions given by OTB developers lead to scarce results. In fact, the procedure proposed in OTB does not allow for the full exploitation of the potential of RPC models, on which OTB itself is based. As OTB is open-source software, a plugin was developed to overcome these limitations and exploit its full potential. In fact, OTB interfaces are unfortunately essential, and some necessary functions are missing. Therefore, a new QGIS plugin was developed in order to run the entire process in the most photogrammetrically and statistically correct way, and, at the same time, to simplify the relative procedures.

TanDEM-X Forest Mapping Using Convolutional Neural Networks

In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear processing through CNN. In particular, we focus on the main InSAR features as the backscatter, coherence, and volume decorrelation, as well as the acquisition geometry through the local incidence angle. By using different state-of-the-art CNN architectures, our experiments consistently demonstrate the great potential of deep learning in data fusion for information extraction in the context of synthetic aperture radar signal processing and specifically for the task of forest mapping from TanDEM-X images. We compare three state-of-the-art CNN architectures, such as ResNet, DenseNet, and U-Net, obtaining a large performance gain over the baseline approach for all of them, with the U-Net solution being the most effective one.

Use of SfM-MVS approach to nadir and oblique images generated throught aerial cameras to build 2.5D map and 3D models in urban areas

The use of Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) approaches to build 3D models of structures belonging to the Cultural Heritage environment is becoming more widespread. Due to the big dimensions of the digital aerial images generated by airborne sensors and, consequently, computation problems, these images were rarely used for the construction of 3D models through SfM-MVS approach. In addition, the values of overlap used in traditional aerial surveys would lead to a geometry of acquisition that is quite weak. For these reasons, the use of aerial images generated by airborne sensors in the SfM-MVS approach was limited. However, taking into account the possibility to acquire aerial nadir and oblique images according to multi-view, the increasing of High Performance Computation and the use of Direct Georeferencing, the quality of the city model obtained with the SfM/MVS approach was evaluated on a dataset of aerial images involving the Old Town of Bordeux.

Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo

Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region of the Earth several times per day with varying geometry. This provides a wealth of information that makes aerosol remote sensing possible when combined with the multi-spectral capabilities of the on-board imagers. Nonetheless, the suitability of geostationary observations for AOD retrieval may vary significantly depending on their spatial, spectral, and temporal characteristics. In this work, the potential of geostationary satellites was assessed based on the concept of critical surface albedo (CSA). CSA is linked to the sensitivity of each spaceborne observation to the aerosol signal, as it is defined as the value of surface albedo for which a varying AOD does not alter the satellite measurement. In this study, the sensitivity to aerosols was determined by estimating the difference between the surface albedo of the observed surface and the corresponding CSA (referred to as dCSA). The values of dCSA were calculated for one year of observations from the Meteosat Second Generation (MSG) spacecraft, based on radiative transfer simulations and information on the satellite acquisition geometry and the properties of the observed surface and aerosols. Different spectral channels from MSG and the future Meteosat Third Generation-Imager were used to study their distinct capabilities for aerosol remote sensing. Results highlight the significant but varying potential of geostationary observations across the observed Earth disk and for different time scales (i.e., diurnal, seasonal, and yearly). For example, the capability of sensing multiples times during the day is revealed to be a notable strength. Indeed, the value of dCSA often fluctuates significantly for a given day, which makes some instants of time more suitable for aerosol retrieval than others. This study determines these instants of time as well as the seasons and the sensing wavelengths that increase the chances for aerosol remote sensing thanks to the variations of dCSA. The outcomes of this work can be used for the development and refinement of AOD retrieval algorithms through the use of the concept of CSA. Furthermore, results can be extrapolated to other present-day geostationary satellites such as Himawari-8/9 and GOES-16/17.

Alignment methods for nanotomography with deep subpixel accuracy.

As the resolution of X-ray tomography improves, the limited long-term stability and accuracy of nanoimaging tools does not allow computing artifact-free three-dimensional (3D) reconstructions without an additional step of numerical alignment of the measured projections. However, the common iterative alignment methods are significantly more computationally demanding than a simple tomographic reconstruction of the acquired volume. Here, we address this issue and present an alignment toolkit, which exploits methods with deep-subpixel accuracy combined with a multi-resolution scheme. This leads to robust and accurate alignment with significantly reduced computational and memory requirements. The performance of the presented methods is demonstrated on simulated and measured datasets for tomography and also laminography acquisition geometries. A GPU accelerated implementation of our alignment framework is publicly available.

Satellite Interferometry as a Tool for Early Warning and Aiding Decision Making in an Open-Pit Mine

Slope failures are life-threatening geohazards well-known in the mining industry, where a single accident can endanger workers and cause million dollars’ -worth of activity downtime. Therefore, a detailed ground deformation map of an open-pit mine represents a vital planning tool and high-frequency monitoring is pivotal, since false or anticipated alarms may represent a higher impact than missed alarms. This article presents an application of interferometric satellite data from Sentinel-1. The scope of the study is showing the potential of the new generation of satellites for the detection of the boundary, typology, and kinematics of the on-going slope instabilities in open-pit mines. Most notably, this experience discloses how the short revisiting time (six days) permits the implementation of time of failure forecasting methods, which so far were a prerogative of ground-based monitoring systems. Only recently, a few experiences have shown the possibility of ( a posteriori ) predicting slope failures using displacement time series measured from satellite; the case study here described represents one of such first examples. Moreover, 11 other areas of instability have been detected and classified in terms of subsidence (sediment compaction) or slope failures, based on considerations concerning the acquisition geometry used to detect the movements. The movement vectors along the ascending and descending lines of sight have been combined to compute horizontal and vertical vectors, which have been employed to infer the geometry of the sliding surface of some selected slope instabilities. Despite the advancements of the technique, clear limitations still exist and are discussed in this article.

A comparison of two reflectivity parametrizations in acoustic least-squares reverse time migration

ABSTRACTIn comparison with conventional reverse time migration (RTM), least-squares RTM (LSRTM) can improve imaging resolution and compensate irregular illumination caused by acquisition geometry and complex structures. Since proposed, as an advanced version of RTM, it has been applied a lot to improve resolution and balance amplitude in imaging. Generally, in LSRTM, there are two kinds of LSRTM reflectivity models: velocity perturbation related reflectivity model and normal-incidence reflection coefficient related reflectivity model. Each has its specific physical meaning and provides different inverted results. In this paper, we first give a brief review about the two different definitions. Then, we compare the differences of these two methods and build a mathematical relationship. In the definition related to reflection coefficient model, we rescale the defined reflectivity with background velocity. Also, in source wavefield reconstruction, we use an effective trick by moving file pointer to fetch data from disk to reduce the memory cost. Finally, we test these two LSRTM schemes using the Marmousi model. We observe that the two inverted reflectivities are different, although they both image the subsurface discontinuities well. We furthermore extract traces from the inversion results and compare them with the true reflectivity models, respectively, to verify the physical definitions of them.

Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift

Probabilistic registration algorithms [e.g., coherent point drift, (CPD)] provide effective solutions for point cloud alignment. However, using the original CPD algorithm for automatic registration of terrestrial laser scanner (TLS) point clouds is highly challenging because of density variations caused by scanning acquisition geometry. In this letter, we propose a new global registration method, introducing the use of the CPD framework for TLS point clouds. We first consider the measurement geometry and the intrinsic characteristics of the scene to simplify points. In addition to the Euclidean distance, we incorporate geometric information as well as structural constraints in the probabilistic model to optimize the so-called matching probability matrix. Among the structural constraints, we use a spectral graph to measure the structural similarity between matches at each iteration. The method is tested on three data sets collected by different TLS scanners. Experimental results demonstrate that the proposed method is robust to density variations and can decrease iterations effectively. The average registration errors of the three data sets are 0.05, 0.12, and 0.08 m, respectively. It is also shown that our registration framework is superior to the state-of-the-art methods in terms of both registration errors and efficiency. The experiments demonstrate the effectiveness and efficiency of the proposed probabilistic global registration.

The Western Australia Modeling project — Part 2: Seismic validation

Large-scale 3D modeling of realistic earth models is being increasingly undertaken in industry and academia. These models have proven useful for various activities such as geologic scenario testing through seismic finite-difference (FD) modeling, investigating new acquisition geometries, and validating novel seismic imaging, inversion, and interpretation methods. We have evaluated the results of the Western Australia (WA) Modeling (WAMo) project, involving the development of a large-scale 3D geomodel representative of geology of the Carnarvon Basin, located offshore of WA’s North West Shelf (NWS). Constrained by a variety of geologic, petrophysical, and field seismic data sets, the viscoelastic WAMo 3D geomodel was used in seismic FD modeling and imaging tests to “validate” model realizations. Calibrating the near-surface model proved to be challenging due to the limited amount of well data available for the top 500 m below the mudline. We addressed this issue by incorporating additional information (e.g., geotechnical data, analog studies) as well as by using soft constraints to match the overall character of nearby NWS seismic data with the modeled shot gathers. This process required undertaking several “linear” iterations to apply near-surface model conditioning, as well as “nonlinear” iterations to update the underlying petrophysical relationships. Overall, the resulting final WAMo 3D geomodel and accompanying modeled shot gathers and imaging results are able to reproduce the complex full-wavefield character of NWS marine seismic data. Thus, the WAMo model is well-calibrated for use in geologic and geophysical scenario testing to address common NWS seismic imaging, inversion, and interpretation challenges.

An Efficient and Accurate GB-SAR Imaging Algorithm Based on the Fractional Fourier Transform

In this paper, an efficient and accurate imaging algorithm is presented for Ground-Based Synthetic Aperture Radar (GB-SAR) or other radar systems that could be formed by a physical or synthetic linear aperture. The imaging algorithm is based on the fractional Fourier transform (FrFT) for the azimuth compression. A mathematical framework is derived according to the projection of a sample reflectivity image onto the pseudopolar coordinate, and its implementation was presented. With the data acquisition geometry and the pseudopolar imaging coordinate, the phase of a point target can be expressed as a quadratic phase exponential. It makes that only 1-D FrFT is needed for the azimuth compression of the time-domain backscatter data for the GB-SAR imaging problem. By further research, the optimal transformation order that represents the spatial frequency changes by the FrFT was given subsequently. Taking advantage of this optimal representation, the proposed approach avoids the large calculation that occurs in the time-domain back projection (TDBP). Comparing to the far-field pseudopolar format algorithm (FPFA), the accuracy of the proposed algorithm is much improved. Meanwhile, the proposed approach holds almost the same computational cost and complexity as the FPFA. The proposed approach keeps the advantages of the imaging quality of the TDBP and the computational cost of the FPFA that are two important aspects of the GB-SAR applications. Both the numerical simulation and the field GB-SAR experiment show that the algorithm is more suitable for the high-precision GB-SAR imaging, especially for the near field.

Flexible algebraic technique for multiview reconstruction: incremental learning in reflective tomography

Reflective tomography reconstructs a scene from calibrated reflective images, using algorithms from x-ray tomography. Many works on the subject are based on analytical formulas, such as the filtered backprojection. However, these formulas require constraints on the acquisition geometry, such as a circular rotation. We want to avoid such constraints; they may be seriously violated in some practical cases. To tackle this problem, we tune the algebraic reconstruction technique from x-ray tomography. More precisely, we look for a model of the scene such that the x-ray projections of the model approximate recorded calibrated reflective images. The model is computed by an iterative algebraic method: a Kaczmarz algorithm. In this way, we perform incremental supervised learning in optics, where the hypothesis space emulates reflective tomography. We get a flexible method for multiple-view reconstruction based on linear algebra. It accepts a general calibrated acquisition, such as several cameras arbitrarily located/oriented, with visible near-infrared wavelengths. It could reconstruct a scene using several devices simultaneously, such as air–ground cameras combined with ground–ground cameras. The relevance of the approach is numerically shown from calibrated CCD images of the Middlebury datasets. In particular, we get reconstructions from 16 views.

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections.

X-ray angiography is the most commonly used imaging modality for the detection of coronary stenoses due to its high spatial and temporal resolution of lumen contour and its utility to guide coronary interventions in real time. However, the high inter- and intra-observer variability in interpreting the geometry of 3D vascular structure based on multiple 2D image projections is a limitation in the accurate determination of lesion severity. This could be addressed by the 3D reconstruction of the coronary arterial (CA) tree. The automated reconstruction of 3D CA tree from 2D projections is challenging due to the existence of several imaging artifacts, such as vessel overlap, foreshortening, and most importantly respiratory and cardiac motion. Along with these artifacts, the acquisition geometry introduces the possibility of generating false vessel segments in the reconstruction. Our approach aims to reduce the motion artifacts in angiographic projections by developing a new method for rigid and non-rigid motion correction. A novel point-cloud based approach is subsequently introduced for reconstruction of 3D vessel centerlines by iteratively minimizing the reconstruction error. The performance of the proposed 3D reconstruction is evaluated using angiographic projections from 45 patients, producing average reprojection errors of 0.092 ±0.055 mm and 0.910 ±0.352 mm for 3D centerlines reconstruction, when co-registered with the parent vessels on projection planes that were/were not used to derive the 3D reconstruction, respectively. A comparison of the reconstructed 3D lumen surface with optical coherence tomography (OCT) measurements has been performed, showing no statistically significant difference in the luminal cross-sections reconstructed with our method, compared to OCT.

A practical workflow using quantitative interpretation of seismic amplitudes to derisk infill wells in deepwater Gulf of Mexico: The Auger example

Seismic direct hydrocarbon indicators (DHIs) are routinely used in the identification of hydrocarbon reservoirs and in the positioning of drilling targets. Understanding seismic amplitude reliability and character, including amplitude variation with offset (AVO), is key to correct interpretation of the DHI and to enable confident assessment of the commercial viability of the reservoir targets. In many cases, our interpretation is impeded by limited availability of data that are often less than perfect. Here, we present a seismic quantitative interpretation (QI) workflow that made the best out of imperfect data and managed to successfully derisk a multiwell drilling campaign in the Auger and Andros basins in the deepwater Gulf of Mexico. Data challenges included azimuthal illumination effects caused by the presence of the Auger salt dome, sand thickness below tuning, and long-term production effects that are hard to quantify without dedicated time-lapse seismic. In addition, seismic vintages with varying acquisition geometries led to different QI predictions that further complicated the interpretation story. Given these challenges, we implemented an amplitude derisking workflow that combined ray-based illumination assessments and prestack data observations to guide selection of the optimal seismic data set(s) for QI analysis. This was followed by forward modeling to quantify the fluid saturation and sand thickness effects on seismic amplitude. Combined with structural geology analysis of the well targets, this workflow succeeded in significantly reducing the risk of the proposed opportunities. The work also highlighted potential pitfalls in AVO interpretation, including AVO inversion for the characterization of reservoirs near salt, while providing a workflow for prestack amplitude quality control prior to inversion. The workflow is adaptable to specific target conditions and can be executed in a time-efficient manner. It has been applied to multiple infill well opportunities, but for simplicity reasons here, we demonstrate the application on a single well target.

Data-Driven Tool Uses Amplitude-Based Statistics To Identify Seismic Fractures

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191668, “Big Data Analytics for Seismic Fracture Identification Using Amplitude-Based Statistics,” by Egbadon Udegbe, SPE, Eugene Morgan, SPE, and Sanjay Srinivasan, SPE, Pennsylvania State University, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. Data-analysis tools for extracting information about critical subsurface features such as fractures are still evolving. Traditional methods rely on time-consuming iterative work flows, which involve computing seismic attributes, denoising, and expert interpretation. Additionally, the increasingly widespread acquisition of time-lapse seismic surveys has led to heightened demand for reliable automated work flows to assist in deriving feature interpretation from seismic data. The authors present a novel data-driven tool for fast fracture identification in post-stack seismic data•sets. Introduction The paper develops an automated work flow for fast and robust fracture identification that directly uses seismic amplitude data as input. Adapted from real-time face-detection methods, the proposed algorithm computes spatiotemporal amplitude statistics using Haar-like bases to characterize the seismic amplitude properties that correspond to fracture occurrence in a unit window. In this approach, the amplitude data are decomposed into a lower-dimensional collection of simple-to-calculate mini-attributes, which contain gradient and curvature characteristics at varying locations and scales. These features serve as inputs to a cascade of boosted classification trees, which select and combine the most-discriminative features to develop a probabilistic binary classification model. This approach helps to eliminate the computationally intensive and subjective use of seismic attributes in existing approaches. Approach The proposed methodology uses a supervised learning approach. This involves specifying examples of seismic amplitude regions that either contain or do not contain fractures and subsequently presenting these examples to a binary classification scheme that develops a set of rules to distinguish between both fracture and nonfracture windows. Finally, using this trained classification model, any arbitrary amplitude section or volume can be scanned to determine the location of fractures on the basis of the rules defined within the specified window. Training-Window Selection. Careful consideration is required in defining the dimensions of the unit window. In 2D, this unit window is a rectangular region parallel to time and space axes. In 3D, the unit window refers to a cuboidal region with one time and two space axes. The dimensions of the window in the vertical (time) direction are influenced by the time-sampling interval and the source wavelet frequency. In the lateral directions, window dimensions are influenced by the spatial sampling interval, which is a result of the survey acquisition geometry. Overall, smaller unit windows lead to higher resolution in identifying fracture presence. This, however, is achieved at the expense of fewer amplitude features to characterize the window, as well as slower scan speeds, because more regions must be visited to make predictions. Once a seismic amplitude data set with known fracture locations and a unit window with specified dimensions has been established, multiple locations within the data set may be visited and amplitude samples extracted from both fractured and nonfractured regions.

Potential for Absolute Ionosphere and Clock Correction in Noncooperative Bistatic SAR

A method is presented to estimate and compensate the ionospheric and clock-drift perturbations that affect bistatic synthetic aperture radar (SAR) images acquired under a quasi-monostatic acquisition geometry. This is accomplished through multisquint-based processing of the interferometric phase, which allows separating the ionospheric component from the rest of the phase perturbations by processing small azimuth subapertures. It is demonstrated how the absolute, i.e., nondifferential, ionospheric phase can be estimated by exploiting the baseline of the acquisition geometry, which further allows an individual correction of each image of the bistatic pair. A mathematical model of the bistatic SAR phase perturbations, as well as an algorithm for performing their estimation and compensation, is presented. The performance of the method is assessed with end-to-end simulations of bistatic acquisitions over synthetic distributed targets.

Three‐dimensional ISAR imaging: a review

Three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging has been proven feasible by combining traditional ISAR imaging and interferometry. Such technique, namely inteferometric ISAR (In-ISAR), allows for the main target scattering centres to be mapped into a 3D spatial domain as point clouds. Specifically, the use of an In-ISAR system can overcome the main geometrical interpretation issues imposed by the monostatic acquisition geometry as the problem of cross-range scaling and unknown image projection plane (IPP). However, some issues remain such as scatterer scintillation, shadowing effects, poor SNR etc., which limit the effectiveness of 3D imaging. A solution to such unsolved issues can be found in the use of multiple 3D views, which can be obtained exploiting either multi-temporal or multi-perspective configurations or a combination of both. This study aims to review the main concepts to produce multi-view 3D ISAR images by using In-ISAR systems also presenting real data collected with a multi-static In-ISAR system.

Intersample distance in radiofrequency-based through-the-wall mapping for acceptable reconstruction quality

The ability to obtain reconstructed floor maps of acceptable quality with less samples speeds up the acquisition phase, which is of the highest relevance for most radio-frequency (RF)-based through-the-wall mapping (TWM) applications, particularly in the area of law enforcement and security. The key issue impacting the acquisition time is the quantity of necessary samples for acceptable floor reconstruction quality, which in turn depends on the intersample distance. We evaluate the impact of enlarging the intersample distance in the reconstruction quality of an algebraic method applied to RF-based TWM, under a parallel-beam acquisition geometry. Finite-element method (FEM) is used to define a physical model, allowing high accuracy in the simulation results. Two test floor maps are built in the FEM model, and the correspondent very-high frequency RF samples are generated in each case, composing the so-called sinograms. The sinograms are forwarded to the algebraic reconstruction method—simultaneous iterative reconstruction technique—to provide estimates of the original floor maps. In simulations, the use of sinogram upsampling to improve image resolution leads to floor maps of acceptable reconstruction quality in a parallel-beam RF-based TWM setup with intersample distances up to two wavelengths for an angular step of 4.5 deg.

Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite

Synthetic aperture radar (SAR) tomography (TomoSAR) techniques exploit multipass acquisitions of the same scene with slightly different view angles, and allow generating fully 3-D images, providing an estimation of scatterers’ distribution along range, azimuth, and elevation directions. This paper extends TomoSAR to geosynchronous SAR (GEO TomoSAR). First, the potential and performance of GEO TomoSAR were analyzed from the perspective of orbital perturbation and the resulting large spatial baseline. Then, the rotation-induced decorrelation problems induced by the along-track baseline component were analyzed. In addition, the optimized acquisition geometry and tomographic processing flow were given, and the computer simulation verification was also completed. Finally, the equivalent validation experiment based on Beidou inclined geosynchronous orbit (IGSO) navigation satellite was carried out to demonstrate the feasibility and effectiveness of GEO TomoSAR. The experimental system employs the Beidou IGSO satellite as illuminator of opportunity and a ground system collecting and processing reflected echoes. This is the first time to employ the data from repeat-track Beidou IGSO satellites for tomographic processing. The 3-D imaging of the urban area using this experimental system was presented and then verified using LiDAR cloud data as reference. The results show that GEO TomoSAR can form the baseline of the order of hundreds of kilometers in elevation, which has the ability to achieve a resolution of 5 m in elevation.

A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy)

This work focuses on the capabilities and limitations of the Advanced Satellite SAR (Synthetic Aperture Radar) Interferometry (A-DInSAR) in wooded and mountainous regions, with the aim to get insights on the performances for studying slow-moving landslides. The considered critical issues are related to the SAR acquisition geometries (angle of incidence of the satellite line of sight, ascending and descending geometries) and to the physical and morphological features of the slopes (land use, aspect and slope angles), which influence the measuring points coverage. 26 areas in Lombardy Region (Italy), affected by known slope instability phenomena, have been analyzed through A-DInSAR technique, using COSMO-SkyMed images. The results allowed to outline general considerations about the effectiveness of A-DInSAR analysis of a single dataset (descending or ascending dataset), selected accordingly to the aspect of the slopes. Moreover, we aimed to quantitatively describe the capability to update the state of activity of several previously mapped landslides using satellite SAR Interferometry results. Although in a wooded and mountainous region, where the chances of retrieving radar targets for satellite SAR analysis are generally low, the A-DInSAR results have allowed to detect landslides’ reactivations or new landslides and to update the inventory for about 70% of the investigated areas.