Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Temporal Insar and Target Detection with COSMO-SkyMed SAR Big Data to Monitor Urban Dynamics in Wuhan (China)

An unprecedented time series of 293 COSMO-SkyMed StripMap SAR images acquired in 2011–2020 is exploited to investigate land subsidence and vehicle traffic in Wuhan, China. Persistent Scatterer Interferometry using linear and non-linear deformation models suggests that the spatial and temporal evolution of subsidence relates with the dynamic urban development across the main city districts. Traffic patterns along bridges were captured by detecting vehicles based on their azimuth shift caused by their across-track motion, and identified by type based on their radar cross section and speed. The results of vehicle counting confirm an increasing number of vehicles over the last years, which is currently an urban challenge for Wuhan.
Self-Supervised Image Colorization for Semantic Segmentation of Urban Land Cover

The task of semantic segmentation plays a central role in the analysis of remotely sensed imagery. This relevance is reflected in the act of classifying each image pixel belonging to a particular class. This allows the acquisition of semantic knowledge in form of a classification map, which facilitates decision-making processes. Nowadays, the task of semantic segmentation is mainly solved with Supervised pre-training. It needs plenty of labels to learn a mapping function, which produces useful features. As alternative, Self-supervised learning (SSL) techniques entirely explore the data, find supervision signals and solve a challenge called Pretext task for coming upon robust representations. The current work investigates Image Colorization (IC) as Pretext task to learn feature representations, which will be transferred to an U-Net for predicting semantic segmentations of urban scenes. The study examines two benchmark datasets for validation and generation of classification maps. The results show that the learned features through colorization achieve accurate segmentation results. This was possible both using unlabeled ImageNet training data and the actual datasets. These contain up to half a million examples, which represents a modest amount compared to the number of annotated images present in ImageNet.
Fusion of Spaceborne and Airborne SAR Images Using Saliency and Fuzzy Logic for Vessel Detection

In the paper, we propose a new method based on multi-order superpixel-level saliency and fuzzy logic (MSSFL) to fuse spaceborne and airborne SAR images for vessel detection. First, we generate a new global regional contrast map (GRCM) by exploiting the multi-order superpixel-level saliency (MSS). In the generated GRCM, the vessel targets are well restored and the backgrounds are suppressed. Next, a new fuzzy logic approach is presented to fuse the MSS information provided by the GRCMs. This GRCM-based fuzzy fusion can further enhance the vessel target regions and filter out the inshore interference regions. Experimental results using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR images show that the proposed MSSFL method yields higher target-to-cluster ratio (TCR) of fused images and improved detection performance compared with the commonly utilized image fusion approaches.
Object Based Image Analysis For Delineation Of Slope Units

This research focuses on proposing an approach for delineation of slope units from topographic parameters of terrain slope, slope aspect and topographic position index (TPI) extracted from ALOS World 3D digital elevation model (DEM). The research was carried out through segmentation processes at four different levels with varying parameters values of scale, size and compactness on RGB composite (TPI, slope, aspect). D-matrix was used to find the optimal parametrization for accuracy measures and showed that the lowest value of $D$ lies with the scale parameter as 50, size as 0.3 and compactness as 0.7. The segmentation results obtained with these parameter values were classified into two landform classes of flat terrain and slope units and compared with the reference watershed boundary delineated using soil and water assessment tool (SWAT). The result showed higher agreement in terms of accuracy with changing slope positions of ridge line, drainage line and valley that can be further used for enhanced landslide susceptibility maps.
Daily Estimation of Inland Water Storage in the Madeira Basin During the Last Twenty Years (1998–2018)

Inland water storage is a key reservoir in the continental water cycle but the scientific knowledge about its spatio-temporal dynamic is still poor, especially over tropical areas. By coupling the Soil and Water Assessment Tool (SWAT) model and the Soil WAter Fraction (SWAF) data to increase the inundation delineation precision, inland water storage in the Madeira Basin located in Southern Amazon Basin was computed from 1998 to 2018 each day. During this period, the maximum of water storage was reached in March every year and varied from $3.01\times 10^{11}\ \mathrm{m}^{3}$ to $1.22\times 10^{11}\ \mathrm{m}^{3}$. The Madeira Basin and each floodplain section have a peculiar temporal hydrologic response. The methodology presented in this paper can be extended to the entire Amazon Basin and other large-scale watersheds.
RSVQA Meets Bigearthnet: A New, Large-Scale, Visual Question Answering Dataset for Remote Sensing

Visual Question Answering is a new task that can facilitate the extraction of information from images through textual queries: it aims at answering an open-ended question formulated in natural language about a given image. In this work, we introduce a new dataset to tackle the task of visual question answering on remote sensing images: this large-scale, open access dataset extracts image/question/answer triplets from the BigEarthNet dataset. This new dataset contains close to 15 millions samples and is openly available. We present the dataset construction procedure, its characteristics and first results using a deep-learning based methodology. These first results show that the task of visual question answering is challenging and opens new interesting research avenues at the interface of remote sensing and natural language processing. The dataset and the code to create and process it are open and freely available on https://rsvqa.sylvainlobry. com/
An Adaptive Parameter-Based Control Technique of Virtual Synchronous Generator for Smooth Transient Between Islanded and Grid-Connected Mode of Operation

This paper deals with the development of a control technique for the control of voltage source converters (VSCs) as an interface between renewable energy sources (RESs) and power grids. The lack of inertia in converter-based generators and their unconventional dynamic behavior increase the negative impacts on the power grid, unlike the operation of the conventional synchronous generators (SGs). In this sense, the proposed control technique emulates the behavior of SGs in the control loop of the interfaced converter acting as a grid-forming power generator. Thus, supportive functionalities for enhancing electric grid stability (e.g., frequency oscillation damping) will be provided by interfaced converter as a virtual synchronous generator (VSG). The main targets of the proposed control technique include virtual inertia emulation, frequency oscillation damping, primary and secondary frequency regulation, and voltage regulation. The main contribution of this work over the existing methods is that the optimal values for swing equation parameters are employed to ensure well-designed damping feature of the VSG-based converter which adapts grid-connected and islanded operating modes, enabling a smooth resynchronizing to the grid as its plug-and-play capability requirement. The control structure is evaluated through simulation in MATLAB/Simulink.
Increasing Flexibility of FPGA-based CNN Accelerators with Dynamic Partial Reconfiguration

Convolutional Neural Networks (CNN) are widely used for image classification and have achieved significantly accurate performance in the last decade. However, they require computationally intensive operations for embedded applications. In recent years, FPGA-based CNN accelerators have been proposed to improve energy efficiency and throughput. While dynamic partial reconfiguration (DPR) is increasingly used in CNN accelerators, the performance of dynamically reconfigurable accelerators is usually lower than the performance of pure static FPGA designs. This work presents a dynamically reconfigurable CNN accelerator architecture that does not sacrifice throughput performance or classification accuracy. The proposed accelerator is composed of reconfigurable macroblocks and dynamically utilizes the device resources according to model parameters. Moreover, we devise a novel approach, to the best of our knowledge, to hide the computations of the pooling layers inside the convolutional layers, thereby further improving throughput. Using the proposed architecture and DPR, different CNN architectures can be realized on the same FPGA with optimized throughput and accuracy. The proposed architecture is evaluated by implementing two different LeNet CNN models trained by different datasets and classifying different classes. Experimental results show that the implemented design achieves higher throughput than current LeNet FPGA accelerators.
Sequences of Sparse Matrix-Vector Multiplication on Fugaku’s A64FX processors

We implement parallel and distributed versions of the sparse matrix-vector product and the sequence of matrix-vector product operations, using OpenMP, MPI, and the ARM SVE intrinsic functions, for different matrix storage formats. We investigate the efficiency of these implementations on one and two A64FX processors, using a variety of sparse matrices as input. The matrices have different properties in size, sparsity and regularity. We observe that a parallel and distributed implementation shows good scaling on two nodes for cases where the matrix is close to a diagonal matrix, but the performances degrade quickly with variations to the sparsity or regularity of the input.
A Multi-Objective Approach for Multi-Channel SAR Despeckling

SAR image interpretation is always impaired by speckle that is a multiplicative noise due to interference among the backscatterings from targets inside a resolution cell. Many algorithms for both single and multi-channel SAR despeckling have been proposed in the last forty years following different approaches. Recently, a multi-objective convolutional neural network, named MONet, has been proposed for single channel SAR despeckling. It relies on a mulit-objectvie cost function that takes into account three main aspects of the SAR images: noise removal, details and statistics preservation. Inspired by MONet, in this paper a deep learning method for InSAR phase filtering is proposed. The idea is to benefit from the multi-objective cost function defined in MONet that seems to perfectly fit with the interferogram denoising. This is the first step towards a solution able to provide a complete processed multi-channel product.
Automated Damaged Buildings Identification from High-Spatial-Resolution Imagery with Texture and Spectral Information

A method is proposed for visualizing damage information of traditional buildings in Chinese village. Firstly, we label the buildings one by one in experimental area. Secondly, the texture and spectral feature parameters of each building are calculated. For each single building, parameters that are positively correlated with the level of damage are normalized to form a parameter vector, and the module was calculated subsequently. Finally, mapping parameter vector and module to RGB color space, and the distribution, damage type and level of each buildings can be visualized. Experimental results show that this method can realize recognizing damage buildings in high-spatial-resolution imagery, and get initial understanding of buildings damage level. In summary, this method can effectively and rapidly realize the extraction of damaged buildings information with high resolution images.
Earth Observation Image Semantics: Latent Dirichlet Allocation Based Information Discovery

Land cover maps are among the most important products of Remote Sensing (RS) imagery. Despite remarkable advancements in land cover classification techniques, abundant detailed information in the very high-resolution RS images necessitates further improvements to harness the data and discover detailed semantic information. Moreover, scarcity of the labelled data and its quality is a major limitation in RS land cover mapping. In the present study, Latent Dirichlet Allocation is employed for semantic discovery in RS images and a novel kernel-based Bag of Visual Words model is proposed for land cover mapping.
A Multi-Model Fusion of Convolution Neural Network and Random Forest for Detecting Settlements Without Electricity

In this paper, a multi-model fusion framework is proposed for automatic detection of settlements without electricity (DSE) based on the multimodal and multitemporal remote sensing data. To settle the problems of data noise and data redundancy, the data preprocessing step, which consists of band selection, cloud removal, grayscale stretch and data augmentation, is firstly applied. Two models of single-task and dual-task are further constructed for DSE. The single-task model builds a global context convolutional neural network (GC-CNN) for the detection of settlements without electricity and the dual-task model employs the GC-CNN for settlement detection and the random forest classifier for electricity detection. Moreover, a model fusion principle and a post-processing method is designed to integrate and improve the results above, thus producing the final segmentation result. Verified through the competition website, the proposed method achieved a F1-score of 0.8806, ranking second in the first track of 2021 IEEE GRSS Data Fusion Contest.
Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

In the field of Compressed Sensing, estimation of sparsity level is very essential as the sparsity level determines the minimum number of (i) measurements to be obtained of a sparse signal during acquisition and (ii) iterations to be performed for many of the greedy techniques for the perfect recovery of the sparse signal from the obtained measurements. In this paper, we propose a Maximum Likelihood (ML) estimator to estimate the instantaneous sparsity level during acquisition and an ML sequence (MLS) estimator of sparsity levels during recovery. As the sparsity level varies in time due to the continuous birth of newer supporting components and death of existing supporting components, this paper models the sparsity level variation as a stochastic birth-death process. The real-world applications of the proposed estimators are presented on the compression of aircraft vibration signals and the estimation of wireless channels. The simulation results on real-world and model-generated data demonstrate the performance merits of the proposed estimators compared to other existing methods.
Determining Iceberg Scattering Mechanisms in Greenland Using Quad Pol ALOS-2 SAR Data

Iceberg properties, together with meteorological and environmental conditions can influence Synthetic Aperture Radar (SAR) backscatter behaviours. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse icebergs in Greenland. We investigate the scattering mechanisms through several observables and decompositions. Our results show that the most common scattering mechanisms for icebergs is surface scattering and volume scattering. Sometimes double bounce is also observed. By performing a multi-scale analysis using boxcar $5 \times 5$ and $11 \times 11$ window sizes, we conclude that icebergs can be a collection of strong scatterers. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future.
A Deep Feature Transformation Method Based on Differential Vector for Few-Shot Learning

Due to the lack of raw data, difficulty in labeling as well as the sensor parameters limitation, few-shot learning in SAR images has become an important research direction. A deep feature transformation method based on differential vector is proposed in this paper to alleviate the feature drift problem in few-shot learning. Firstly, samples generated by a modified Adversarial Auto Encoder (AAE) are introduced as an auxiliary dataset for the few-shot training dataset. Secondly, differential vector is further proposed to alleviate the cross-class-bias between the generated and real data in deep feature space. It is defined as the mean difference of the low-dimensional feature vectors of the real and generated samples, which is considered to be able to describe the transformation direction from generated to real data. Experiments conducted on MST AR dataset demonstrate the feasibility, effectiveness and superiority of proposed method.
Comparison of Target Detectors to Identify Icebergs in Quad- Polarimetric Sar Alos-2 Images

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire under cloud cover and during polar nights. Additionally, polarimetry has been proven to improve the detection capability. In this work, we compare six state-of-the-art quad polarimetric detectors to test their performance and ability to detect small sized icebergs in four locations in Greenland. These were the polarimetric notch filter (PNF), polarimetric match filter (PMF), polarimetric whitening filter (PWF), optimal polarimetric detector (OPD), reflection symmetry detector, and the dual polarisation anomaly detector (iDPolRAD). We use four single look complex ALOS-2 quad pol images. The data were calibrated and processed. We produce the covariance matrices of each image before applying a testing and training window for detection. We also add a guard window to reduce false alarms. Our results show that the multi-look polarimetric whitening filter and optimal polarimetric detector provide the most optimal performance in quad and dual pol mode detection.
BIOMASS Level-2 Products - Part I: Rationale and Applications

This paper describes the rationale and development of the estimation techniques for the level-2 data products of the European Space Agency's 7th Earth Explorer BIOMASS mission. BIOMASS is planned for launch in 2023 and will carry the first-ever P-band synthetic aperture radar (SAR) onboard a satellite. It has been designed to produce consistent global maps of the Earth's forests during a nominal five-year lifetime. Fully polarimetric SAR data will be collected and the satellite orbit will be selected for repeat-pass interferometry and tomography in separated mission phases. Mission requirements call for three level-2 data products: above-ground biomass, forest height and forest disturbance. The paper also discusses the expected limitations of the estimation techniques and remaining problems to be addressed.
A Superpixel-Based Neighborhood Polarimetric Covariance Matrix for Polsar Ship Detection

In a recent work, a neighborhood polarimetric covariance matrix [N] was proposed to detect ships from polarimetric SAR (PolSAR) imagery. However, its computational complexity is extremely high. Besides, the heterogeneity surrounding ship edges is also not well considered in [N]. To cure these draw-backs, we construct a novel superpixels-based neighborhood polarimetric covariance matrix [SN] in this paper. Specifically, the simple linear iterative clustering (SLIC) is first used to obtain superpixels. Then, the vector vmean corresponding to the mean value of superpixel is further computed so as to characterize the neighborhood information of each pixel in superpixel. Finally, by combining the original scattering vector v and vmean together, the vector t12 is built to calculate [SN]. The experiment tested on one L-Band ALOS PolSAR imagery shows that i) the polarimetric whitening filter derived from [SN] (i.e., PWFSN) has a better detection performance than that derived from [N] (i.e., PWFN); ii) the calculation process of [SN] takes much less time than that of [N].
Video Formation Method for UAV SAR Utilizing Tensor Recovery Algorithm

Video synthetic aperture radar (SAR) have received more and more attention in recent years as it can provide continuous images of the observed area. However, the enormous data generated by the multi-frame images in video SAR brings big challenges to its transmission, storage and processing, especially for small unmanned aerial vehicle (UAV) platform. In this paper, we aim at proposing an efficient video formation method for SAR system with reduced data. The video formation problem is modelled as a joint low-rank and sparse tensors recovery problem. After that, this problem is solved by an efficient tensor recovery method based on alternating direction method of multiplier. Compared with frequency-domain or time-domain imaging methods, the amount of data samples used can be greatly reduced. Numerical simulations validate the effectiveness of the proposed method.
Recent Development of Unpowered Exoskeletons for Lower Extremity: A Survey

Unpowered exoskeletons (UEs) have attracted extensive research attentions for their portability, handleability and simplicity. However, designing exoskeletons without any active actuators is a great challenge because essential criteria such as performance enhancement, low impedance, and comfort must be met. This paper reviews the UEs researches from three aspects including modeling for understanding gait pattern, mechanical designs, and validation methods, which are main considerations of designing UEs. This review is based on the reported works in the past two decades and underlines some practical opportunities and challenges. Statistical data analysis and critical reviews are stated to understand human gait patterns, designing of UEs, and ergonomic validation. This review is trying to inspire a common understanding about passive exoskeletons for lower extremity and promote discussion among researchers, developers, or robotic practitioners.
A Modified SHMPWM Algorithm based on the 3L-NPC Converter Connected with the 10kV Grid

Multilevel inverters have recently received increased attention in high-power applications since they can generate high-quality output signals at low switching frequencies. The three-level neutral-point clamped (3L-NPC) inverter is one of multilevel topologies which is formed from the series connection of IGBTs. In order to seek to improve reliability and efficiency of high-power applications, switching losses and total harmonic distortion (THD) should be reduced in power inverters. Pre-programmed pulse width modulation (PWM) methods, such as selective harmonic mitigation (SHMPWM) and selective harmonic elimination (SHEPWM) techniques, can enable the harmonic profile of the output waveform to be preprogrammed over a range of modulation indices. In this paper, a modified SHMPWM control strategy for NPC-based inverter applications is presented to obtain the switching patterns, which is capable of satisfying grid codes under equal dc link voltages. This method is based on the use of feasible solutions near the regions of infeasible solutions to obtain feasible solutions in these regions. Additionally, the adjustment of the safety factors in the mathematical functions of SHMPWM technique is also applied to try to obtain feasible solutions as much as possible. Finally, several simulation results are provided to verify the effectiveness of the proposed SHMPWM algorithm.