Full Spatial Resolution Research Articles

Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations

This article introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. The architecture successfully captured PV local volt-volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Encoding complex amplitude information onto phase-only diffractive optical elements using binary phase Nyquist gratings

We reexamine a simple technique for encoding complex amplitude information onto a phase-only spatial light modulator (SLM). The basis for the approach is to spatially vary the diffraction efficiency of a two-dimensional checkerboard binary phase diffraction grating where the period for the Nyquist grating is two pixels. As the phase depth of this 2D grating changes spatially, the amount of light diffracted into the zero order can be controlled. Unwanted information is encoded onto the first diffraction orders and is directed away from the center. This process uses a very simple coding algorithm to generate a complex beam reconstruction on-axis and allows exploiting the full spatial resolution for encoding amplitude. However, its experimental realization with the current liquid-crystal on silicon (LCOS) technology is strongly affected by the limitations imposed by the fringing effect in these devices. We provide experimental evidence of how this effect impacts the efficiency of diffraction gratings displayed on the SLM. We then show how it affects the encoding technique, both in the near field and in the Fourier transform domain, where the limitations imposed by the fringing effect are clearly visible in the form of a focused peak. These results provide evidence of the usefulness of the technique but also about the limitations imposed by the current LCOS technology, which do not allow fully exploiting their high resolution. Finally, we discuss the performance of these newer LCOS devices compared to other SLMs.

Distributed Polarization Measurement for Fiber Sensing Coils: A Review

Distributed polarization measurement is a significant way to evaluate polarization-maintaining fiber. It can assess the fiber's polarization preserving ability and locate internal defects and external perturbations of a long-distance PM fiber. Distributed polarization crosstalk of a fiber-optic polarization device accurately describes its time-dependent optical performance. Therefore, distributed polarization measurement technology can be used to predict and prevent the performance degradation and failure. However, the remaining shortcomings of existing measurement methods are low dynamic range, short measurement length, inadequate spatial resolution, and low diagnostic ability. This paper reviews recent advances in solving the above issues and presents ways to obtain high performances: a dynamic range > 90 dB, a measurement length > 10 km, a full range spatial resolution <; 10 cm, and developments in time domain, frequency domain, and time-frequency domain diagnosis methods. All these high-performance techniques have led to more effective diagnosis of fiber optic sensing coils. This can be demonstrated by a series of comparative measurements and analysis of three PM fiber optic sensing coils in various configurations such as a randomly-wound spool of fiber and a free-standing coil quadrupole-pattern-wound from it, a second coil with and without a flange at various temperatures, and a third coil spliced to a Y waveguide. Each is one of the key configurations during the production processes or the application of the fiber sensing coil. The technical progress reviewed here will greatly promote the development, production, and application of fiber sensing coils.

Electron-event representation data enable efficient cryoEM file storage with full preservation of spatial and temporal resolution.

Direct detector device (DDD) cameras have revolutionized electron cryomicroscopy (cryoEM) with their high detective quantum efficiency (DQE) and output of movie data. A high ratio of camera frame rate (frames per second) to camera exposure rate (electrons per pixel per second) allows electron counting, which further improves the DQE and enables the recording of super-resolution information. Movie output also allows the correction of specimen movement and compensation for radiation damage. However, these movies come at the cost of producing large volumes of data. It is common practice to sum groups of successive camera frames to reduce the final frame rate, and therefore the file size, to one suitable for storage and image processing. This reduction in the temporal resolution of the camera requires decisions to be made during data acquisition that may result in the loss of information that could have been advantageous during image analysis. Here, experimental analysis of a new electron-event representation (EER) data format for electron-counting DDD movies is presented, which is enabled by new hardware developed by Thermo Fisher Scientific for their Falcon DDD cameras. This format enables the recording of DDD movies at the raw camera frame rate without sacrificing either spatial or temporal resolution. Experimental data demonstrate that themethod retains super-resolution information and allows the correction of specimen movement at the physical frame rate of the camera while maintaining manageable file sizes. The EER format will enable the development of new methods that can utilize the full spatial and temporal resolution of DDD cameras.

Estimation of Canopy Gap Fraction from Terrestrial Laser Scanner Using an Angular Grid to Take Advantage of the Full Data Spatial Resolution

This paper develops an algorithm to estimate vegetation canopy gap fraction (GF), taking advantage of the full Terrestrial Laser Scanner (TLS) resolution. After calculating the TLS angular resolution, the algorithm identifies the missing laser hits (gaps) within an angular grid in the azimuthal and zenithal directions. The algorithm was first tested on angular data simulations with random (R), cluster (C) and random and cluster together (RC) gap pattern distributions. Noise introduced in the simulations as a percentage of the resolution accounted for the effect of TLS angular uncertainty. The algorithm performs accurately if angular noise is &lt;6% of the angular resolution. To assess the impact of the change in projection, this study compared these GF estimates from angular grid simulations to their transformation into simulated hemispherical images (SHI). SHI with C patterns perform accurately, but R and RC patterns underestimate GF, especially for GF values below 0.6. The SHI performance to estimate GF was always far below the algorithm developed here with the angular grid simulations. When applied to actual TLS data acquired over individual Quercus ilex L. trees, the algorithm rendered a GF between 0.26 and 0.40. TLS had an angular noise &lt;6%. Converting the angular grid into simulated HI (TLS-SHI) provided a better agreement with actual HI acquired in the same location as the TLS data, since they are in the same projection. The TLS-SHI underestimated GF by an average of 4% compared to HI. HI and TLS-SHI presented 14% and 17% lower values than the GF calculated from the angular grids, respectively. Nevertheless, the results from the simulations indicate that the algorithm based on the angular grid should be closer to the actual GF of the tree canopy.

Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks.

Resting-state functional magnetic resonance imaging (rs-fMRI) data are 4-dimensional volumes (3-space + 1-time) that have been posited to reflect the underlying mechanisms of information exchange between brain regions, thus making it an attractive modality to develop diagnostic biomarkers of brain dysfunction. The enormous success of deep learning in computer vision has sparked recent interest in applying deep learning in neuroimaging. But the dimensionality of rs-fMRI data is too high (~20 M), making it difficult to meaningfully process the data in its raw form for deep learning experiments. It is currently not clear how the data should be engineered to optimally extract the time information, and whether combining different representations of time could provide better results. In this paper, we explored various transformations that retain the full spatial resolution by summarizing the temporal dimension of the rs-fMRI data, therefore making it possible to train a full three-dimensional convolutional neural network (3D-CNN) even on a moderately sized [~2,000 from Autism Brain Imaging Data Exchange (ABIDE)-I and II] data set. These transformations summarize the activity in each voxel of the rs-fMRI or that of the voxel and its neighbors to a single number. For each brain volume, we calculated regional homogeneity, the amplitude of low-frequency fluctuations, the fractional amplitude of low-frequency fluctuations, degree centrality, eigenvector centrality, local functional connectivity density, entropy, voxel-mirrored homotopic connectivity, and auto-correlation lag. We trained the 3D-CNN on a publically available autism dataset to classify the rs-fMRI images as being from individuals with autism spectrum disorder (ASD) or from healthy controls (CON) at an individual level. We attained results competitive on this task for a combined ABIDE-I and II datasets of ~66%. When all summary measures were combined the result was still only as good as that of the best single measure which was regional homogeneity (ReHo). In addition, we also applied the support vector machine (SVM) algorithm on the same dataset and achieved comparable results, suggesting that 3D-CNNs could not learn additional information from these temporal transformations that were more useful to differentiate ASD from CON.

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

Space-time-resolved Breit-Wheeler process for a model system

We study the creation process of an electron-positron pair as a result of the collision between two incoming photons with full spatial and temporal resolution. The dynamics of the four involved particles is described by a simplified model based on a Yukawa Hamiltonian in one spatial dimension. This quantum field theoretical approach permits us to go beyond the usual external field approximation and to study the depletion of the two colliding photon wave packets due to the back reaction of the created electron-positron pair. If the spatial extension of the incoming photons is sufficiently narrow, we predict an interesting blueshift with regard to the optimum incoming photon momentum that can maximize the pair-creation yield close to the threshold momentum.

Dual-Tap Computational Photography Image Sensor With Per-Pixel Pipelined Digital Memory for Intra-Frame Coded Multi-Exposure

A coded-exposure-pixel image sensor for computational imaging applications is presented. Each frame exposure time is divided into ${N}$ subframes. Within each subframe, each pixel sorts photo-generated charge into two charge taps depending on that pixel’s 1-bit binary code. ${N}$ global updates of arbitrary pixel-wise codes are implemented in each frame to enable ${N}$ short global pixel-specific subexposures within one frame. To make these subexposures global, two latches per pixel are utilized in a pipelined fashion. The code for the next subframe is loaded into latch 1 in a row parallel fashion, while the code for the current subframe is being applied by latch 2 globally for photo-generated charge sorting during the current subexposure. A $280^{H}\times 176^{V}$ image sensor prototype with $11.2{\text{-}}\mu \text{m}$ pixel pitch has been fabricated in a $0.11{\text{-}}\mu \text{m}$ CMOS image sensor (CIS) technology. The image sensor has been demonstrated in two computational photography applications, each using only a single frame of a video: 1) computing both albedo (a measure of reflectivity) and 3-D depth maps by means of structured-light imaging and 2) computing surface normals (3-D orientations) map by means of photometric stereo imaging. These demonstrations experimentally validate some of the unique capabilities of this computational image sensor, such as accurate 3-D visual scene reconstruction using only one camera, while maintaining its native specifications: the full spatial resolution and the maximum frame rate.

Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction

Both a high spatial and a high temporal resolution of images and videos are desirable in many applications such as entertainment systems, monitoring manufacturing processes, or video surveillance. Due to the limited throughput of pixels per second, however, there is always a trade-off between acquiring sequences with a high spatial resolution at a low temporal resolution or vice versa. In this paper, a modified sensor concept is proposed which is able to acquire both a high spatial and a high temporal resolution. This is achieved by dynamically reading out only a subset of pixels in a non-regular order to obtain a high temporal resolution. A full high spatial resolution is then obtained by performing a subsequent three-dimensional reconstruction of the partially acquired frames. The main benefit of the proposed dynamic readout is that for each frame, different sampling points are available which is advantageous since this information can significantly enhance the reconstruction quality of the proposed reconstruction algorithm. Using the proposed dynamic readout strategy, gains in PSNR of up to 8.55 dB are achieved compared to a static readout strategy. Compared to other state-of-the-art techniques like frame rate up-conversion or super-resolution which are also able to reconstruct sequences with both a high spatial and a high temporal resolution, average gains in PSNR of up to 6.58 dB are possible.

Multispectral video acquisition using spectral sweep camera.

This paper proposes a novel multispectral video acquisition method for dynamic scenes by using the Spectral-Sweep camera. To fully utilize the redundancies of multispectral videos in the spatial, temporal and spectral dimensions, we propose a Complex Optical Flow (COF) method that could extract the spatial and spectral signal variations between adjacent spectral-sweep frames. A complex L 1-norm constrained optimization algorithm is proposed to compute the COF maps, with which we recover the entire multispectral video by temporally propagating the captured spectral-sweep frames under the guidance of reconstructed COF maps. We demonstrate the promising accuracy of reconstructing full spatial and temporal sensor resolution multispectral videos with our method both quantitatively and qualitatively. Compared with state-of-the-art multispectral imagers, our computational multispectral imaging system can significantly reduce the hardware complexities, while achieves comparable or even better performance.

An adaptive local time-stepping scheme for multiresolution simulations of hyperbolic conservation laws

We present an adaptive local time-stepping (ALTS) scheme for a block-structured multiresolution scheme of hyperbolic conservation laws for fluid flow. The stability of standard local time-stepping (LTS) schemes with level-dependent time-step sizes is improved by local time-step size adaptation when progressing through the underlying multi-stage time integration scheme. The novelty of the approach is that it merges flux computation and time integration of the state vector with projection and prediction operations of the multiresolution scheme [15]. This enables consistent time integration of subdomains with different refinement levels without the need for intermediate time synchronization which can be prohibitively expensive in parallel computations. Consequently, coarser subdomains are advanced in time only once finer subdomains have advanced to the same time instant. Full spatial resolution adaptivity for integrated regions after each substep is maintained.The new scheme exhibits significantly improved numerical stability as compared to previous LTS schemes due to the local time-step size adaptation at each substep. The computational overhead of the incurred additional operations is small. In applications, the ALTS scheme demonstrates the same computational efficiency as standard LTS schemes.The new scheme can be applied to any explicit single-step time-integration scheme and is independent of the employed spatial discretization scheme. The improved stability is demonstrated with several one- and two-dimensional examples of flows with one and two phases, applying second- and third-order Runge-Kutta time integration schemes.

Using TNT-NN to unlock the fast full spatial inversion of large magnetic microscopy data sets

Modern magnetic microscopy (MM) provides high-resolution, ultra-high-sensitivity moment magnetometry, with the ability to measure at spatial resolutions better than 10^{-4} m and to detect magnetic moments weaker than 10^{-15} Am^2. These characteristics make modern MM devices capable of particularly high-resolution analysis of the magnetic properties of materials, but generate extremely large data sets. Many studies utilizing MM attempt to solve an inverse problem to determine the magnitude of the magnetic moments that produce the measured component of the magnetic field. Fast Fourier techniques in the frequency domain and non-negative least-squares (NNLS) methods in the spatial domain are the two most frequently used methods to solve this inverse problem. Although extremely fast, Fourier techniques can produce solutions that violate the non-negativity of moments constraint. Inversions in the spatial domain do not violate non-negativity constraints, but the execution times of standard NNLS solvers (the Lawson and Hanson method and Matlab’s lsqlin) prohibit spatial domain inversions from operating at the full spatial resolution of an MM. In this paper, we present the applicability of the TNT-NN algorithm, a newly developed NNLS active set method, as a means to directly address the NNLS routine hindering existing spatial domain inversion methods. The TNT-NN algorithm enhances the performance of spatial domain inversions by accelerating the core NNLS routine. Using a conventional computing system, we show that the TNT-NN algorithm produces solutions with residuals comparable to conventional methods while reducing execution time of spatial domain inversions from months to hours or less. Using isothermal remanent magnetization measurements of multiple synthetic and natural samples, we show that the capabilities of the TNT-NN algorithm allow scans with sizes that made them previously inaccesible to NNLS techniques to be inverted. Ultimately, the TNT-NN algorithm enables spatial domain inversions of MM data on an accelerated timescale that renders spatial domain analyses for modern MM studies practical. In particular, this new technique enables MM experiments that would have required an impractical amount of inversion time such as high-resolution stepwise magnetization and demagnetization and 3-dimensional inversions.

Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis

Tropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 400–1040 nm) and shortwave-infrared (SWIR, 1210–2365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pan-sharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producer’s accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producer’s accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images.

Using Persistent Scatterer Interferometry to Map and Quantify Permafrost Thaw Subsidence: A Case Study of Eboling Mountain on the Qinghai‐Tibet Plateau

AbstractPermafrost thaw subsidence, a key indicator of permafrost degradation, remains poorly quantified or understood. It is particularly challenging to detect and measure surface subsidence due to the loss of subsurface ice over a large area because it usually develops gradually, over several years or decades. Here we utilize the persistent scatterer interferometric synthetic aperture radar (PSI) approach to remotely measure gradual surface subsidence on Eboling Mountain in the northeastern region of the Qinghai‐Tibet Plateau, where thermal erosion gullies are well developed. Most of the previous multitemporal interferometric synthetic aperture radar studies on permafrost used the small baseline subset method. By contrast, the PSI approach benefits from the full spatial resolution and is less affected by temporal or geometric decorrelation. In the PSI analysis, we incorporate a piecewise elevation change model that includes periodic subsidence/uplift because of its seasonally varying components as well as its linear subsidence trends. Applying this permafrost‐designated PSI algorithm to 17‐L band ALOS‐1 PALSAR images taken between 2006 and 2011, we find that both the thermal erosion gullies and the surrounding regions (within about 300 m) subside gradually. The subsidence trends range from 0.3 to 3 cm/yr. This suggests that permafrost areas near the gullies are more vulnerable to gradual thawing and degradation. This study demonstrates the potential of using PSI to study permafrost thaw processes and of assessing its impacts over vast areas on the Qinghai‐Tibet Plateau and in the Arctic.

FC-RCCN: Fully convolutional residual continuous CRF network for semantic segmentation

Enlarging the spatial resolution of features generated by fully convolutional networks (FCNs) can improve the performance of semantic segmentation. To achieve this goal, deeper network with deconvolutional structure can be applied. However, when the network architecture becomes more complex, the training efficiency may degrade. To address the joint optimization problem of improving spatial resolution through deeper networks and training deeper networks more effectively, we propose a Fully Convolutional Residual Continuous CRF Network (FC-RCCN) for semantic segmentation. FC-RCCN is composed of three subnetworks: a unary network, a pairwise network, and a superpixel based continuous conditional random filed (C-CRF) network. In order to generate full spatial resolution predictions with high-quality, a residual block based unary network with multi-scale features fusion is proposed. Even though the unary network is a deeper network, the whole framework can be trained effectively in an end-to-end way using the joint pixel-level and superpixel-level supervised learning strategy which is optimized by a pixel-level softmax cross entropy loss and a superpixel-level log-likelihood loss. Besides, C-CRF inference is fused with pixel-level prediction during the test procedure, which guarantees the method’s robustness to the superpxiel errors. In the experiments, we evaluatee the power of the three subnetworks and the learning strategy comprehensively. Experiments on three benchmark datasets demonstrate that the proposed FC-RCCN outperforms previous segmentation methods and obtains the state-of-the-art performance.

Advanced SBAS-DInSAR Technique for Controlling Large Civil Infrastructures: An Application to the Genzano di Lucania Dam.

Monitoring surface deformation on dams is commonly carried out by in situ geodetic surveying, which is time consuming and characterized by some limitations in space coverage and frequency. More recently microwave satellite-based technologies, such as advanced-DInSAR (Differential Synthetic Aperture Radar Interferometry), have allowed the integration and improvement of the observation capabilities of ground-based methods thanks to their effectiveness in collecting displacement measurements on many non-destructive control points, corresponding to radar reflecting targets. The availability of such a large number of points of measurement, which are distributed along the whole structure and are characterized by millimetric accuracy on displacement rates, can be profitably adopted for the calibration of numerical models. These models are implemented to simulate the structural behaviour of a dam under conditions of stress thus improving the ability to maintain safety standards. In this work, after having analysed how advanced DInSAR can effectively enhance the results from traditional monitoring systems that provide comparable accuracy measurements on a limited number of points, an FEM model of the Genzano di Lucania earth dam is developed and calibrated. This work is concentrated on the advanced DInSAR technique referred to as Small BAseline Subset (SBAS) approach, benefiting from its capability to generate deformation time series at full spatial resolution and from multi-sensor SAR data, to measure the vertical consolidation displacement of the Genzano di Lucania earth dam.

Enhancing SDO/HMI images using deep learning

Context.The Helioseismic and Magnetic Imager (HMI) provides continuum images and magnetograms with a cadence better than one per minute. It has been continuously observing the Sun 24 h a day for the past 7 yr. The trade-off between full disk observations and spatial resolution means that HMI is not adequate for analyzing the smallest-scale events in the solar atmosphere.Aims.Our aim is to develop a new method to enhance HMI data, simultaneously deconvolving and super-resolving images and magnetograms. The resulting images will mimic observations with a diffraction-limited telescope twice the diameter of HMI.Methods.Our method, which we callEnhance, is based on two deep, fully convolutional neural networks that input patches of HMI observations and output deconvolved and super-resolved data. The neural networks are trained on synthetic data obtained from simulations of the emergence of solar active regions.Results.We have obtained deconvolved and super-resolved HMI images. To solve this ill-defined problem with infinite solutions we have used a neural network approach to add prior information from the simulations. We testEnhanceagainst Hinode data that has been degraded to a 28 cm diameter telescope showing very good consistency. The code is open source.

On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI

The detectability of adjacency effects (AE) in ocean color remote sensing by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI is theoretically assessed for typical observation conditions up to 36km offshore (20km for MSI). The methodology detailed in Bulgarelli et al. (2014) is applied to expand previous investigations to the wide range of terrestrial land covers and water types usually encountered in mid-latitude coastal environments. Simulations fully account for multiple scattering within a stratified atmosphere bounded by a non-uniform reflecting surface, sea surface roughness, sun position and off-nadir sensor view. A harmonized comparison of AE is ensured by adjusting the radiometric sensitivity of each sensor to the same input radiance. Results show that average AE in data from MODIS-A, and from MERIS and OLCI in reduced spatial resolution, are still above the sensor noise level (NL) at 36km offshore, except for AE caused by green vegetation at the red wavelengths. Conversely, in data from the less sensitive SeaWiFS, OLI and MSI sensors, as well as from MERIS and OLCI in full spatial resolution, sole AE caused by highly reflecting land covers (such as snow, dry vegetation, white sand and concrete) are above the sensor NL throughout the transect, while AE originated from green vegetation and bare soil at visible wavelengths may become lower than NL at close distance from the coast. Such a distance increases with the radiometric resolution of the sensor. It is finally observed that AE are slightly sensitive to the water type only at the blue wavelengths. Notably, for an atmospheric correction scheme inferring the aerosol properties from NIR data, perturbations induced by AE at NIR and visible wavelengths might compensate each other. As a consequence, biases induced by AE on radiometric products (e.g., the water-leaving radiance) are not strictly correlated to the intensity of the reflectance of the nearby land.