High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery

Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.EC/H2020/734541/EU/TOOLS FOR MAPPING HUMAN EXPOSURE TO RISKY ENVIRONMENTAL CONDITIONS BY MEANS OF GROUND AND EARTH OBSERVATION DATA/EOXPOSUR

Non-local tensor completion for multitemporal remotely sensed images inpainting

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method

Constrained Tensor Decompositions for SAR Data: Agricultural Polarimetric Time Series Analysis

Tensor decompositions are a powerful tool for multidimensional data analysis, interpretation, and signal processing. This work develops a constrained tensor decomposition framework for complex multidimensional Synthetic Aperture Radar (SAR) data. The framework generalizes the Canonical Polyadic (CP) decomposition by formulating it as an optimization problem and allows precise control over the shape and properties of the output factors. The implementation supports complex tensors, automatic differentiation, different loss functions, and optimizers. We discuss the importance of constraints for physical validity, interpretability, and uniqueness of the decomposition results. To illustrate the framework, we formulate a polarimetric time series decomposition and apply it to data acquired over agricultural areas to analyze the development of four crop types at X, C, and L bands over the period of twelve weeks. The obtained temporal factors describe the changes in the crops in a compact way and show a correlation to certain crop parameters. We extend the existing polarimetric time series change analysis with the decomposition to show the changes in more detail and provide an interpretation through the polarimetric factors. The decomposition framework is extensible and promising for joint information extraction from multidimensional SAR data

CloudScout: A deep neural network for on-board cloud detection on hyperspectral images

The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. On the test set we achieve 92% of accuracy with 1% of False Positives (FP). The Phisat-1 mission will start in 2020 and will operate for about 6 months. It represents the first in-orbit demonstration of Deep Neural Network (DNN) for data processing on the edge. The innovation aspect of our work concerns not only cloud detection but in general low power, low latency, and embedded applications. Our work should enable a new era of edge applications and enhance remote sensing applications directly on-board satellite

Multipass SAR Interferometry Based on Total Variation Regularized Robust Low Rank Tensor Decomposition

Multipass SAR interferometry (InSAR) techniques based on meter-resolution spaceborne SAR satellites, such as TerraSAR-X or COSMO-SkyMed, provide 3D reconstruction and the measurement of ground displacement over large urban areas. Conventional methods such as persistent scatterer interferometry (PSI) usually requires a fairly large SAR image stack (usually in the order of tens) to achieve reliable estimates of these parameters. Recently, low rank property in multipass InSAR data stack was explored and investigated in our previous work (J. Kang et al., “Object-based multipass InSAR via robust low-rank tensor decomposition,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 6, 2018). By exploiting this low rank prior, a more accurate estimation of the geophysical parameters can be achieved, which in turn can effectively reduce the number of interferograms required for a reliable estimation. Based on that, this article proposes a novel tensor decomposition method in a complex domain, which jointly exploits low rank and variational prior of the interferometric phase in InSAR data stacks. Specifically, a total variation (TV) regularized robust low rank tensor decomposition method is exploited for recovering outlier-free InSAR stacks. We demonstrate that the filtered InSAR data stacks can greatly improve the accuracy of geophysical parameters estimated from real data. Moreover, this article demonstrates for the first time in the community that tensor-decomposition-based methods can be beneficial for large-scale urban mapping problems using multipass InSAR. Two TerraSAR-X data stacks with large spatial areas demonstrate the promising performance of the proposed method

Object-based multipass InSAR via robust low-rank tensor decomposition

The most unique advantage of multipass synthetic aperture radar interferometry (InSAR) is the retrieval of long-term geophysical parameters, e.g., linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed by Kang, as an alternative to the typical single-pixel methods, e.g., persistent scatterer interferometry (PSI), or pixel-cluster-based methods, e.g., SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a follow-on, this paper investigates the inherent low rank property of such phase tensors and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition. We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g., PSI, by a factor of 10-30 in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits, in turn, can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks

Impact of Etna’s volcanic emission on major ions and trace elements composition of the atmospheric deposition

Mt. Etna, on the eastern coast of Sicily (Italy), is one of the most active volcanoes on the planet and it is widely recognized as a big source of volcanic gases (e.g., CO2 and SO2), halogens, and a lot of trace elements, to the atmosphere in the Mediterranean region. Especially during eruptive periods, Etna’s emissions can be dispersed over long distances and cover wide areas. A group of trace elements has been recently brought to attention for their possible environmental and human health impacts, the Technology-critical elements. The current knowledge about their geochemical cycles is still scarce, nevertheless, recent studies (Brugnone et al., 2020) evidenced a contribution from the volcanic activity for some of them (Te, Tl, and REE). In 2021, in the framework of the research project “Pianeta Dinamico”, by INGV, a network of 10 bulk collectors was implemented to collect, monthly, atmospheric deposition samples. Four of these collectors are located on the flanks of Mt. Etna, other two are in the urban area of Catania and three are in the industrial area of Priolo, all most of the time downwind of the main craters. The last one, close to Cesarò (Nebrodi Regional Park), represents the regional background. The research aims to produce a database on major ions and trace element compositions of the bulk deposition and here we report the values of the main physical-chemical parameters and the deposition fluxes of major ions and trace elements from the first year of research. The pH ranged from 3.1 to 7.7, with a mean value of 5.6, in samples from the Etna area, while it ranged between 5.2 and 7.6, with a mean value of 6.4, in samples from the other study areas. The EC showed values ranging from 5 to 1032 μS cm-1, with a mean value of 65 μS cm-1. The most abundant ions were Cl- and SO42- for anions, Na+ and Ca+ for cations, whose mean deposition fluxes, considering all sampling sites, were 16.6, 6.8, 8.4, and 6.0 mg m-2 d, respectively. The highest deposition fluxes of volcanic refractory elements, such as Al, Fe, and Ti, were measured in the Etna’s sites, with mean values of 948, 464, and 34.3 μg m-2 d-1, respectively, higher than those detected in the other sampling sites, further away from the volcanic source (26.2, 12.4, 0.5 μg m-2 d-1, respectively). The same trend was also observed for volatile elements of prevailing volcanic origin, such as Tl (0.49 μg m-2 d-1), Te (0.07 μg m-2 d-1), As (0.95 μg m-2 d-1), Se (1.92 μg m-2 d-1), and Cd (0.39 μg m-2 d-1). Our preliminary results show that, close to a volcanic area, volcanic emissions must be considered among the major contributors of ions and trace elements to the atmosphere. Their deposition may significantly impact the pedosphere, hydrosphere, and biosphere and directly or indirectly human health

Impact of geogenic degassing on C-isotopic composition of dissolved carbon in karst systems of Greece

The Earth C-cycle is complex, where endogenic and exogenic sources are interconnected, operating in a multiple spatial and temporal scale (Lee et al., 2019). Non-volcanic CO2 degassing from active tectonic structures is one of the less defined components of this cycle (Frondini et al., 2019). Carbon mass-balance (Chiodini et al., 2000) is a useful tool to quantify the geogenic carbon output from regional karst hydrosystems. This approach has been demonstrated for central Italy and may be valid also for Greece, due to the similar geodynamic settings. Deep degassing in Greece has been ascertained mainly at hydrothermal and volcanic areas, but the impact of geogenic CO2 released by active tectonic areas has not yet been quantified. The main aim of this research is to investigate the possible deep degassing through the big karst aquifers of Greece. Since 2016, 156 karst springs were sampled along most of the Greek territory. To discriminate the sources of carbon, the analysis of the isotopic composition of carbon was carried out. δ13CTDIC values vary from -16.61 to -0.91‰ and can be subdivided into two groups characterized by (a) low δ13CTDIC, and (b) intermediate to high δ13CTDIC with a threshold value of -6.55‰. The composition of the first group can be related to the mixing of organic-derived CO2 and the dissolution of marine carbonates. Springs of the second group, mostly located close to Quaternary volcanic areas, are linked to possible carbon input from deep sources