Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO₂ gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

In this paper, we introduce two spatially adaptive filtering methods to improve the estimation of the covariance matrix (CM), which is required for the processing of tomographic SAR data. We evaluate their effect on scatterer separation and height estimation. We propose several criteria to evaluate such methods and introduce a spatial simulation procedure allowing generating a tomographic image stack from a 3-D building model, assuming a multitrack airborne configuration and a distributed target model incorporating multidimensional speckle. Inversion of such a model requires the estimation of a CM from the data. Consequently, we propose two nonlocal methods to improve the estimation of the CM. The first one was previously introduced for polarimetric data and uses pixel similarities based on Riemannian distances between CMs. The second one is a new method extending the previous one to similarities between patches. We show the importance of spatial adaptivity in covariance estimation by comparing the 3-D reconstructions obtained with our filters and other methods. Further experiments on simulated and L-band experimental data show the ability of the nonlocal filters to improve the height estimation and scatterer separation in layover areas thanks to their smoothing and edge-preserving properties.

Tomographic SAR is a technique that allows to extend synthetic aperture radar (SAR) imaging to the third dimension by using several images of a scene acquired from different sensor positions. Three-dimensional point clouds extracted thanks to tomographic processing methods are often corrupted by noise and artifacts that need to be corrected. In this paper, we propose a simple convex optimization formulation that exploits the geometric constraint that the line of sight between a sensor and a surface measurement must not be blocked by another surface. We demonstrate the ability of our method to denoise point clouds and fill holes on both synthetic data and experimental E-SAR data by the German Aerospace Center (DLR).

In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extracted from the tomograms. We also provide a comparison of classification results obtained from covariance matrices versus tomogram features as well as obtained by different reference methods, i.e., the traditional Wishart classifier and the more sophisticated Random Forest. Extensive qualitative and quantitative results are shown on a fully polarimetric and multi-baseline dataset from the E-SAR sensor from the German Aerospace Center (DLR).

This paper describes the analysis of complex scenario of urban area by means of a reduced number of tomographic SAR data. First, the tomographic data are processed using the standard MUSIC algorithm, which requires an estimation of the data covariance matrix and an estimation of number of sources. We introduce the tomographic bilateral filter (To-moBLF) to improve the estimation of the covariance matrix and we propose a new model order selection scheme using the tomographic entropy parameters TomoH. Then point of interest are extracted using the Tomographic Signal-to-Noise Index (TomoSNI) algorithm.

TomoSAR (Tomographic SAR) is a technique allowing to extend SAR imaging to the third dimension by using several images of a scene acquired from different sensor positions. 3D point clouds extracted thanks to tomographic processing methods are often corrupted by noise and artifacts which need to be corrected. In this paper, we propose a simple convex optimization formulation that exploits the geometric constraint that the line of sight between a sensor and a surface measurement must be unobscured. We demonstrate the ability of our method to denoise point clouds and fill holes on both synthetic and experimental DLR E-SAR data.