Miracle-dz/UniFAR-FTUs huggingface dataset:
Azimuth Ambiguities Suppression for Multichannel SAR Imaging Based on $\boldsymbol{L_{2,q}}$ Regularization: Initial Results of Non-Sparse Scenario

The azimuth multichannel SAR is competent to achieve high-resolution and wide-swath (HRWS) imaging. For some spaceborne multichannel SAR systems, the pulse repetition frequency (PRF) of each channel at some beam positions is less than that of uniform sampling, hence leading to the azimuth ambiguities in the recovered images. In this paper, a novel azimuth ambiguity suppression method for multichannel SAR imaging based on $L_{2,q}$ regularization $(0 < q\leq 1)$ is proposed. First, by analyzing the reasons of azimuth ambiguities in multichannel SAR, we establish the imaging model different from that in single-channel SAR system. Second, we extend the $L_{2,q}$ regularization from single-channel SAR system to multichannel SAR system and develop the proposed method. Finally, we demonstrate the effectiveness of the proposed method for non-sparse scenarios. Simulations and Gaofen-3 real data experiments are carried out to verify the validity of proposed method.
Accuracy of Sea Surface Temperature from SMR of the HY-2B Compared with In-Situ Data in 2020

Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. It carries several payloads, which includes a scanning microwave radiometer (SMR). Sea surface temperature (SST) is one of the main products of SMR. This study compares SST derived from SMR to in-situ SSTs in all of 2020. The collocations of HY-2B SRM and insitu SSTs were generated with the spatial window of 25km and the temporal window of 30 min, and the matchup data set includes 2696012 points from all over the global sea surface areas. Results show that SMR SST has a mean bias of 0.068°C (SMR minus buoy) and root-mean-square error (RMSE) of 0.851 °C in the global ocean area. By analyzing the global bias distribution map, we can conclude that the larger SST bias points mainly concentrated along the coast, sea ice, and high wind speeds areas where deteriorated the global overall accuracy performance. To prove it, three different areas were selected and analyzed separately. The results show that the RMSE in open ocean over mid and low latitudes is 0.66°C, while RMSEs in the nearshore zone and high wind speeds areas are 0.8°C and 1.1 °C, respectively.
Classification of Oil Spills and Look-Alikes from Sar Images Using Bag of Visual Words Method of Feature Extraction

Synthetic Aperture Radar (SAR), is an active sensor imaging technology and due to its all-day, all-weather imaging capability, it can be effectively used for the detection of oil spills over the ocean. Oil spills, due to their low backscattered energy towards the SAR sensor, appear as dark spots/areas in SAR images. However, a range of unrelated meteorological and oceanographic phenomena also have same spectral signature and appear as dark spots resulting in misidentification. Hence, a major focus of research in this area is the development of algorithms to distinguish oil spills from ‘look-alikes'. The success of correct oil spill detection depends on the feature extraction of all the dark spots and classifying them as oil spill and lookalikes based on their features. This paper describes the development of a new approach to SAR oil spill detection using Bag of Visual Words (BOVW) method of feature extraction and classification. An image dataset having 152 scenes of oil spills and look-alikes each is used to train and evaluate the classifier performance. Overall accuracy of 93 % was obtained for classification. An analysis of BOVW method of feature extraction and classification in this paper has highlighted the importance of Speeded Up Robust Features (SURF) features used by the algorithm for accurately classifying the oil spills and look-alikes.
Brain Extraction From Brain MRI Images Based on Wasserstein GAN and O-Net

Brain extraction is an essential pre-processing step for neuroimaging analysis. It is difficult to achieve high-precision extraction from low-quality brain MRI images with artifacts and gray inconsistencies which often result in irregular hole regions in the extracted brain tissues. In addition, the U-Net based brain extraction method trends to output over-smoothed brain boundary. To remove those irregular holes in the extracted mask, we proposed a new U-Net based model for brain extraction named O-Net. O-Net replaces the skip-connection path in the U-Net with dual shortcut paths including an attention module of an O-shaped network, which uses deep semantic information to highlight the target area while retaining more image details. O-Net effectively reduces the impact of intensity differences caused by artifacts or gray inconsistencies in the brain MRI images on the extraction results. To more accurately identify brain boundary, we designed a new GAN based brain extraction method, which used above O-Net as the segmentation network. The discrimination network of the proposed GAN model adopts the residual structure to enhance the nonlinear expression ability of the network to balance the adversarial training of the two networks. To speed up the convergence of the proposed model, a segmentation loss was added to the adversarial loss to supervise the feature learning of the segmentation network. This method was compared with other popular brain extraction methods on two public datasets (IBSR18 and LPBA40). The mean Dice similarity coefficients obtained by the proposed method were 97.26% and 98.29% on IBSR18 and LPBA40 respectively. In the comparative experiment, the results of the proposed method are the best on the two public datasets. Experimental results show that the proposed model can stably output high-precision brain tissue extraction images and the influence of artifacts and gray inconsistencies is small.
Multi-Scale Building Instance Extraction Framework in High Resolution Remote Sensing Imagery Based on Feature Pyramid Object-Aware Convolution Neural Network

Building extraction based on high-resolution remote sensing imagery has been widely used in automatic surveying and mapping. Recently, the instance segmentation algorithm has been introduced to the building extraction, which can calculate the number and area of buildings simultaneously. However, there are some challenges: 1) multi-scale buildings; 2) occlusion by other adjacent buildings. In this paper, to solve these problems, we propose a multi-scale building instance extraction framework based on feature pyramid object-aware convolution neural network (CNN). In order to solve the multi-scale problem, a feature pyramid CNN is proposed, which combines features from both the bottom-up and top-down architectures. In order to solve the occlusion problem, a multi-scale object-aware instance proposal network is proposed, which introduces the multiscale attention mechanism to aware objects. The experiments conducted on two public datasets and a self-constructed dataset of Changzhou show that the proposed method can achieve an excellent performance.
Similarity Measure with Additional Modality Information for Multimodal Remote Sensing Images

This paper considers the problem of learning efficient similarity measure (SM) for multimodal remote sensing (RS) images. It is desirable to have a single SM that is efficient for different combinations of modes. We first consider the influence of training dataset balancing on SM efficiency. We demonstrate that it is possible to improve overall SM performance. However, this improvement is observed only for some combinations of modes. To cope with this problem, we propose to include information about the modes compared as additional input to the proposed Convolutional Neural Network (CNN). With this additional information, SM performance for all combinations of modes was improved. We confirm SM efficiency improvement for the real data from Sentinel 2, Landsat 8, Hyperion, SIR-C, and Sentinel 1 platforms, ASTER Global DEM 2, and ALOS World 30m global DEMs and for combinations of modes including optical-to-optical, optical-to-radar, optical-to-DEM and radar-to-DEM and compare the proposed CNN performance with existing SMs.
A Robotic Torso Joint With Adjustable Linear Spring Mechanism for Natural Dynamic Motions in a Differential-Elastic Arrangement

To be operated in unknown or complex environments, modern robots have to fulfill various challenging criteria. Among them, one finds requirements such as a high level of robustness to withstand impacts and the capabilities to physically interact in a safe manner. One way to achieve that is to integrate variable-stiffness actuators into the systems, enabling compliant behavior through the elastic components and providing the additional adaptability of the impedance. Here, we introduce a novel adjustable linear stiffness joint mounted in a differential-elastic arrangement. The mechanism is integrated into the anthropomorphic upper body of the DLR David robot and responsible for the spinal rotation. Consequently, the actuator is crucial for the overall workspace of the robot and the realization of energy-efficient natural motions such as in dynamic running. The proposed hardware setup is experimentally validated in terms of the linearity in the spring characteristics, intrinsic damping, the excitation of resonance frequencies, and the ability to alter these resonance frequencies through stiffness adaptation during dynamic motions.
Semantic Segmentation of Land Use / Land Cover (LU/LC) Types Using F-CNNS on Multi-Sensor (Radar-Ir-Optical) Image Data

Land Use/ Land Cover (LU/LC) segmentation is a widely studied topic in the field of remote sensing. Past focus has been on independent studies either on color (RGB) and the Normalized Vegetation Index (NDVI) or on Polarimetric Synthetic Aperture Radar (PolSAR) data. In this paper we explore the fusion potential of RGB images with additional SAR and Near Infra-red (NIR) images for enhanced LU/LC segmentation through Fully-Convolutional Neural Networks (F-CNNs). F-CNNs have been extensively studied for semantic segmentation problems with U-Net and SegNet being two well-known F-CNN architectures. Both these architectures were used as references for this study. High resolution RGB, SAR and NIR images were acquired through Google Earth (GE), German Aerospace Center (DLR) and The Planet Laboratories, respectively. IR was converted to NDVI for its higher potential of segmentation of vegetations areas. Four multi-sensor configurations as input channels to the networks were studied after precise co-registration of these images, and the results were compared to individual channels for both architectures. Simon Fraser University (SFU), Burnaby Campus and its surrounding area was selected for this study due its diverse land types. The area was divided into 5 classes i.e. Roads, Buildings, Forest, Water and No class (unclassified). An overall, best accuracy of ~86% was achieved for a five-channel configuration (R+G+B+SAR+NDVI). We show that the inclusion of SAR and IR channels to RGB based network can significantly improve the performance of LU/LC segmentation.
Simulation of a Self-Regulating Eddy-Current Brakes with Permanent Magnets

This paper describes the possibility of control and regulating the electromagnetic torque of the eddy current brakes (ECBs) with permanent magnets (PMs). This achieved by dividing conducting disc rotor on several parts that are connected by springs. Because of interaction between centrifugal forces and spring forces, the rotor parts can take a place in working air gap where the PMs create magnetic field. Since centrifugal forces are determined by the rotational speed, self-regulation of the brake torque is realized. The paper presents the results of simulation of a magnetic field and a method for assessing the electromagnetic moment.
Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.
Olive Tree Water Stress Detection Using Daily Multispectral Imagery

Daily calibrated multispectral imagery (Planet Fusion) of an olive irrigation deficit trial was used to assess the degree and speed to which vegetation indices indicate water stress. We developed normalization techniques to increase sensitivity to differences across a grove. The normalized difference vegetation index (NDVI) was able to significantly detect differences between the control and deficit treatments for the Arbequina variety. For the Picual variety, the green red vegetation index (GRVI) was the best indicator. Though multispectral imagery is not as quick at indicating irrigation deficits as in-field sensor data, it is complementary in being able to capture the spatial variability of water stress.
A Deep Interactive Framework for Building Extraction in Remotely Sensed Images Via a Coarse-to-Fine Strategy

The performance of building extraction in remotely sensed images has been hugely improved with the development of convolutional neural networks and especially the semantic segmentation field. Due to the rich context of the image scene and the way of labeling (based on the pixel-level predicted probability), the segmentation masks are not always regular or close to the real building boundaries. In order to solve this problem, we propose a simple but effective deep framework based on two stages: the coarse result with an automatic semantic segmentation network and the fine result with an interactive refinement network. By using the binary mask of the initial segmentation and the interactions provided by the users, we obtain the final building extraction result through a deep interactive segmentation network. We evaluate our method on the WHU building dataset, and the results show that the method achieves better performance than the state-of-the-art methods.
Saturated Output-Feedback Hybrid Reinforcement Learning Controller for Submersible Vehicles Guaranteeing Output Constraints

In this brief, we propose a new neuro-fuzzy reinforcement learning-based control (NFRLC) structure that allows autonomous underwater vehicles (AUVs) to follow a desired trajectory in large-scale complex environments precisely. The accurate tracking control problem is solved by a unique online NFRLC method designed based on actor-critic (AC) structure. Integrating the NFRLC framework including an adaptive multilayer neural network (MNN) and interval type-2 fuzzy neural network (IT2FNN) with a high-gain observer (HGO), a robust smart observer-based system is set up to estimate the velocities of the AUVs, unknown dynamic parameters containing unmodeled dynamics, nonlinearities, uncertainties and external disturbances. By employing a saturation function in the design procedure and transforming the input limitations into input saturation nonlinearities, the risk of the actuator saturation is effectively reduced together with nonlinear input saturation compensation by the NFRLC strategy. A predefined funnel-shaped performance function is designed to attain certain prescribed output performance. Finally, stability study reveals that the entire closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and can provide prescribed convergence rate for the tracking errors so that the tracking errors approach to the origin evolving inside the funnel-shaped performance bound at the prescribed time.
Comparison of Polarimetric Filters to Retrieve Forest Biomass

There are several polarimetric filters in the literature developed for the most diverse applications. Here, we present an evaluation of different filter sizes and types for improving the AGB estimates in the Brazilian Amazon, varying from none to 21×21 px filter size in full polarimetric ALOS/PALSAR-2. The optimal window size was chosen by the highest coefficient of determination (R2) between forest AGB and backscattering coefficient (σ°). After that, six polarimetric filters were evaluated: BoxCar, Refined Lee, Improved Sigma Lee, Intensity Driven Adaptive Neighbourhood (IDAN), Scattering Model-Based (SMB), and Model-based (MB). The comparison criterion was a set of statistics aimed at preserving the three basic principles of the filtering process. The optimal window size was 11×11 px. The best was Refined Lee, followed by BoxCar and SMB. The R2 differences in the filter choice can be up to 15% for retrieving forest AGB.
Community Development of the Snow Microwave Radiative Transfer Model for Passive, Active and Altimetry Observations of the Cryosphere

The Snow Microwave Radiative Transfer (SMRT) model was initially developed to explore the sensitivity of microwave scattering to snow microstructure for active and passive remote sensing applications. Here, we discuss the modular design of SMRT that has enabled its rapid extension by the community. SMRT can now represent a layered medium consisting of snow, land ice, lake ice and/or sea ice overlying a substrate of soil, water or parameterized by reflectivity. A time-dependent radiative transfer solution method has also been added to allow for low resolution mode altimetry applications. We illustrate the use of SMRT to simulate brightness temperature for snow on lake ice, backscatter for snow on soil and altimeter waveforms for snow on sea ice.
Land Cover Semantic Segmentation of High-Resolution Gaofen-3 SAR Image

Land cover classification with SAR images mainly focuses on the utilization of fully polarimetric SAR (PolSAR) images. This paper explores the potential of semantic segmentation of high-resolution single polarimetric (single-pol) SAR and PolSAR images, in particular tailored for the Gaofen-3 (GF-3) sensor. First of all, a unified SAR data preprocessing method is utilized to deal with the L2 format SAR data. Then, an encoder-decoder network based on transfer learning is designed to implement semantic segmentation of GF-3 SAR images. Experiments on single-pol SAR and PolSAR images demonstrate the feasibility of semantic segmentation with high-resolution GF-3 images.
A New Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data in Heterogeneous Area

Landsat and MODIS imagery have been widely employed for many applications because of their rich archives and free availability, however, the trade-off between the spatial and temporal resolutions has limited their capacities in monitoring detailed spatio-temporal dynamics. Linear spatio-temporal fusion methods which consider the varieties between fine- and coarse-resolution data as linear can effectively solve this problem, and the coefficients of the linear model are key parameters for fusion accuracy. The existing linear fusion methods either adopt regression algorithms to fit these parameters or set these parameters as constants, both of which may introduce some errors. In this article, we proposed a new method to eliminate the influences of parameters calculation. Instead of calculating the slope and intercept of the linear model directly, these parameters can be both eliminated by equation transformation. In addition, spectrally similar pixels and their corresponding weights were used to mitigate block effects. Compared to the STARFM method, the proposed method can get better fusion accuracy.
Hyperspectral Image Classification Based on Class Confusion Merging and Soft Band Selection

In hyperspectral image (HSI) classification, the distinction of similar classes has always been a focus of research. In this paper, a new classification module named class confusion merging (CCM) is proposed to improve the classification accuracy, especially for classes with the similar spectral feature. In CCM processing, the merging matrix is firstly constructed based on the confusion matrix to measure the similarity between different classes. Then similar classes are merged as big categories. Finally, for each big category, soft band selection is implemented based on the spectral difference of contained classes for reclassification. To evaluate the performance of CCM, real image experiments are conducted in comparison with no CCM module hyperspectral classification methods. The experiment results demonstrate that the CCM module can improve the classifier performance by providing higher classification accuracy.
Evaluation of Four New Land Surface Temperature (LST) Products in the U.S. Corn Belt: ECOSTRESS, GOES-R, Landsat, and Sentinel-3

Asa critical indicator of crop water stress, land surface temperature (LST) has been widely used in agricultural applications for monitoring and quantifying crop stress and crop water use. Given the availability of several newly operational satellite LST products in recent years and an increasing demand for reliable LST for agricultural applications, a systematic and thorough study of evaluation of different satellite LST products for croplands is highly needed. This article, thus, evaluated both the new satellite LST products including ECOsystem Spaceborne Thermal Radiometer on the International Space Station (ECOSTRESS), GOES-R, Landsat Provisional and Sentinel-3, and several mainstream LST products including MODIS Land Surface Temperature/Emissivity product (MOD11A1 and MYD11A1), MODIS/Aqua Land Surface Temperature/3-Band Emissivity product (MYD21A1), and VIIRS/NPP Land Surface Temperature and Emissivity LST (VNP21A1), for agricultural landscapes in the U.S. Corn Belt. The evaluation was benchmarked on in situ measurements from 11 SURFRAD or eddy covariance sites in the growing seasons of 2018 and 2019. Results showed that the nighttime and daytime biases of all LST products on different sites were generally within ±2 °C and ±3 °C, respectively. Regarding the daytime LST, the highest agreement with ground observations was achieved by ECOSTRESS with an overall absolute bias <0.9 °C and a root-mean-squared error (RMSE) < 2.3 °C. MOD11A1 and MYD11A1 products slightly underestimated daytime LST with an overall absolute bias < 0.9 °C and RMSE < 2.9 °C. MYD21A1, Landsat Provisional LST, GOES-R, VNP21A1 and Sentinel-3 LST achieved an overall absolute bias < 2 °C and RMSE < 3.6 °C in terms of daytime LST. Regarding nighttime LST, all LST products reached a low overall absolute bias < 0.5 °C and RMSE < 1.7 °C except ECOSTRESS and MOD11A1 (absolute bias < 1.9 °C and RMSE < 3.4°C). We observed that only ECOSTRESS, MOD11A1, and MYD11A1 achieved the daytime absolute bias < 1 °C. The significant discrepancies between the accuracies of nighttime and daytime LST products indicate that the algorithms of satellite LST products need further improvement under spatial thermal heterogeneous conditions. We further investigated the temporal resolution of cloud-free LST data for the study region, and we found that the cloud-free LST data availability follows the order GOES-R (9.32/day) > MYD11A1 ≈ MOD11A1 (0.37/day) > Sentinel-3 (0.26/day) > Landsat Provisional (0.12/day) > ECOSTRESS (0.07/day). Holistically considering factors of bias, RMSE, spatial resolution and cloud-free data availability of different LST products, MOD11A1, and MYD11A1 are relatively appropriate for agriculture-related applications.