Satellite Synthetic Aperture Radar Images Research Articles

A Dataset of Ground Vehicle Targets from Satellite SAR Images and Its Application to Detection and Instance Segmentation

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

UNet Combined With Attention Mechanism Method for Extracting Flood Submerged Range

Synthetic aperture radar (SAR) satellite has been widely applied in real-time flood monitoring as that they are not affected by extreme weather conditions. However, there is no automatic method to quickly and accurately extract flood areas with SAR satellite images. In this article, a UNet combined with the attention mechanism (UNet-CBAM) method has been proposed for extracting flood submerged areas, and both Longgan Lake and Dahuchi in Poyang Lake Basin are selected as the test sites. Based on the polarization characteristics of two Sentinel-1A data of Poyang Lake, both UNet and UNet-CBAM extraction methods are utilized to extract the flood areas, respectively. Compared with traditional SAR image water extraction methods, simulation results demonstrate that UNet can obtain more satisfactory results in recall, precision, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${F}_1$</tex-math></inline-formula> -parameter, but it has no capability to guarantee continuity in edges and small bodies of water. Moreover, our proposed UNet-CBAM method can further improve recall, precision, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${F}_1$</tex-math></inline-formula> -parameter, respectively. Specifically, when compared with UNet, its recall is increased by 0.8% and 1.2% while <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${F}_1$</tex-math></inline-formula> -parameter is improved by 0.6% and 0.8%, respectively, in the two test sites.

A Robust Two-Stage Registration Algorithm for Large Optical and SAR Images

Accurate optical and synthetic aperture radar (SAR) image registration is crucial to multisensor remote sensing applications. Though several algorithms have been proposed, the practical implementation that directly matches large and high-resolution optical and SAR images remains underexplored by the community. Equipped with the satellite positioning parameters, optical and SAR images can be roughly registered based on geographic coordinates. However, the relative positioning accuracy is still dozens, even hundreds of pixels due to the inaccuracies of sensor parameters and elevation. Consequently, we propose a robust registration algorithm, which consists of two stages, where the horizontal positioning errors can be reduced in the first stage, and then, we fine-tune the correspondences in the second stage. Specifically, we propose a novel template matching method based on the dilated convolutional feature (DCF) and epipolar-oriented phase correlation. DCF is constructed by a depthwise-separable dilated convolution with multichannel gradients, which are generated by the Sobel operator for optical images and a ratio of exponentially weighted averages (ROEWA) operator for SAR images. Due to the large reception field of dilated convolution, DCF can retain invariance even for large relative positioning errors. The epipolar-oriented rectangle template, which stretches along the epipolar line, is then proposed to capture more overlapping areas compared to square templates. Furthermore, the outlier removal is implemented in the coordinate system of optical images to avoid the effect of range compression in SAR images. Inliers are finally used to refine the rational polynomial coefficients (RPCs) based on the bundle adjustment technique. Experimental results on high-resolution optical and SAR image products of various scenarios demonstrate the effectiveness of the proposed registration framework. The relative positioning errors of the refined RPCs can be reduced from hundreds of pixels to the subpixel level.

The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning

This study developed a deep-learning-based model to retrieve sea surface hurricane winds from synthetic aperture radar (SAR) imagery. We introduce the essential idea, residual learning, of the Residual Net into the artificial neural network and design a deep cross-layer concatenation network. The model inputs include SAR measured physical parameters in backscattering energy, the texture feature represented by the grey level co-occurrence matrix, and the morphological hurricane feature. We collected 45 satellite SAR images from Sentinel-1 over hurricane conditions. These images were divided into 39 and 6 for model development and independent testing. A total of 16,127 wind samples acquired from 39 SAR images and simultaneously measured by the Stepped Frequency Microwave Radiometer were collected as model tuning datasets, among which 80% and 20% were used for training and validation. Our validation results show that the deep-learning-based model achieved a correlation coefficient (CORR) and root-mean-square error (RMSE) of 0.98 and 1.72 m/s for wind speeds up to 75 m/s. We further applied the model to six independent SAR images. The model significantly outperformed two existing geophysical algorithms and one backpropagation neural network algorithm with the RMSE is 2.61 m/s and a CORR of 0.95. Moreover, statistical analysis in different wind speed regimes indicates that our model shows a stable performance improvement than comparable algorithms. The RMSE decreases 10%~ 70%, especially the reduction of RMSE is more than 45% at high wind speed (> 42 m/s). Furthermore, adding an independent rainfall estimate to the deep-learning model further enhanced the wind retrieval algorithm.

Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images

Synthetic Aperture Radar (SAR) Sentinel-1 (S1) surface soil moisture (SSM) retrieval is promising but challenging in canopy areas. Additional data from optical-thermal infrared (TIR) sensors (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) have potential to compensate for SSM monitoring from single S1 product, suggesting their synergistic use for SSM retrieval in vegetated terrains. A physical, straightforward and non-calibration synergy method of SAR S1 and optical-TIR MODIS was proposed in this study to enhance SSM estimation in an agro-pastoral transitional zone. Change detection for S1 backscatter (σ°) and simplified triangle method representing land surface temperature (LST) – normalized difference vegetation index (NDVI) space (Ts-VI) for MODIS, were combined under a synergistic framework regarding vegetation conditions. The synergy method performance was evaluated against in-situ soil moisture sites for different phenological stages. Correlation analysis results indicated that S1 σ° is highly sensitive to SSM in non-growing season, whereas MODIS LST is negatively linked to root-zone soil moisture during summer crop growth as the seasonal vegetation effect that can be explained via the observed trapezoidal Ts-VI shape. For the synergy, S1 is mainly involved in during non-growing period, while major contribution in growing period was from MODIS, which accounted for 90% improvements in correlation (R, 0.29 – 0.55) and 30% for Root-Mean-Square Error (RMSE, 0.093 – 0.065 m3m−3) during a four-month crop growth as compared to single S1 product. For entire period, the synergy method outperformed single S1 over all sites, where the average R and RMSE improved 30% (0.46 – 0.60) and 14% (0.070 – 0.060 m3m−3), respectively, with highest values (R = 0.73 and RMSE = 0.039 m3m−3) observed in a maize site. This synergy suggests a suitability of integrating optical-TIR data to improve SAR SSM estimation in agricultural lands, especially over the crop growth period when water is a major limiting factor.

Improved Consistency of an Automated Multisatellite Method for Extracting Temporal Changes in Flood Extent

Malaysia is affected by floods almost every year. In this situation, high-frequency flood monitoring is crucial so that timely measures can be taken. However, the low revisit time of the satellites, as well as occlusion cast by clouds in optical images, limits the frequency of flood observation of the focused area. Therefore, this study proposes utilising multisatellite data from optical satellites such as Landsat 7, Landsat 8, and Moderate Resolution Imaging Spectroradiometer (MODIS), as well as Synthetic Aperture Radar (SAR) images from Advanced Land Observation Satellite (ALOS-2) and Sentinel-1, to increase observation of flood. The main objective was to utilize Otsu image segmentation over both optical and SAR satellite images to distinguish water and nonwater areas in each image separately. For this, modified normalized difference water index (MNDWI) for the optical satellite and total dual-polarization backscatter for SAR satellite images were estimated. The focused area has been divided into Universal Transverse Mercator (UTM) square-size grids of 30 pixels, and each satellite image was reprojected and resampled with a pixel size of 0.001° to standardize the flood map resolution. The second objective was to assess the potential of image fusion for increasing the consistency of water area extraction. Two pairs of satellite images with the same observation period covering a flood event in September 2017 in Perlis, Malaysia, were processed using 2D wavelet transform. Lastly, the temporal changes of the integrated surface water extent were evaluated by comparing the output from both multisatellite and fused images with the observed water level data from the Department of Drainage and Irrigation. The results showed that the proposed model can be used to estimate flood duration as well as to estimate the flood-related losses, especially in ungauged or data-poor regions.

Mapping of Spatio-Temporal Changes of Surface Water Using Sentinel-1 SAR Images

Exploration of surface water bodies and their spatial definition has great importance in water resources management and helps understand hydrological processes in the country. Fast, regular, and effective ways of mapping watercourses and their surroundings through remote sensing methods are crucial tools for capturing change and predicting hazards. The last decades have brought new data products, open-source software, and evaluation procedures that allow low-cost mapping of surface water objects. A widespread and sought-after option for this type of mapping is the use of SAR (Synthetic Aperture Radar) image products. Just through SAR technology that it is possible to identify changes in water in a relatively short time and at the same time under any meteorological conditions thanks to backscattered microwave radiation. This paper presents the possibilities of using SAR technology and its data for long-term temporal mapping of meteorological-hydrological changes in surface water using satellite images of Sentinel-1 product level GRD. As regards surface water extraction, this process is performing by segmenting the threshold values according to the Otsu principle. The water surfaces are then interpreted into the form of water masks of objects by the binarization of the final image. These values are subsequently compared with the supervised classification RFC (Random Forest Classifier) method results. Suitable processing and evaluation procedures conclude that the more suitable polarization configuration for mapping water bodies is VV (vertical-vertical) polarization. As for speckle filter tools to eliminate radar noise, the most suitable option seems to be using a Lee filter. To achieve more accurate results of the extracted water bodies, it is then appropriate to implement quantitative statistical indicators of accuracy and their numerical interpretation of the reliability of results. This paper aims to demonstrate the advantages of using satellite SAR images for spatiotemporal mapping of surface water in the landscape to observe hydrological processes due to inundation, ecological and meteorological changes, and anthropogenic activity.

Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

Optimization Design of Non-Integer Decimation Filter for Compressing Satellite Synthetic Aperture Radar On-board Data

The on-board processor of satellite Synthetic Aperture Radar(SAR) digitizes the back-scattered echoes and transmits them to the ground. As satellite SAR image of various operating conditions including broadband and high resolution is required, an enormous amount of SAR data is generated. Decimation filter is used for data compression to improve the transmission efficiency of these data. Decimation filter is implemented with the FIR(Finite Impulse Response) filter and here, the decimation ratio and tap length are constrained by resource requirements of FPGA used for implementation. This paper suggests to use a non-integer ratio decimation filter in order to optimize the data transmission efficiency. Also, it proposes a filter design method that remarkably reduces the resource constraints of the FPGA in-use via applying a polyphase filter structure. The required resources for implementing the proposed filter is analysed in this paper.

Scenario analysis as a decision-support tool: a maritime patrol case study

This article argued for the use of scenario analysis as a decision-support tool, regarding incentives and investments on either the development or the acquisition of new military systems. In particular, it presented a study on the maritime patrol operations scenario with the availability of synthetic aperture radar satellite images during its planning process. Through a proper analysis, the images may serve as a powerful subsidy for enhancing mission effectiveness and reducing costs. The effectiveness may be measured by the number of encountered vessels and the cost reductions may come from the fewer flight hours necessary for the mission through path optimization. As a conclusion, not only it was possible to observe clear improvements on the mission success, but also the potential this kind of analysis has for force sizing and capability development.

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

Oil spills represent one of the major threats to marine ecosystems. Satellite synthetic-aperture radar (SAR) sensors have been widely used to identify oil spills due to their ability to provide high resolution images during day and night under all weather conditions. In recent years, the use of artificial intelligence (AI) systems, especially convolutional neural networks (CNNs), have led to many important improvements in performing this task. However, most of the previous solutions to this problem have focused on obtaining the best performance under the assumption that there are no constraints on the amount of hardware resources being used. For this reason, the amounts of hardware resources such as memory and power consumption required by previous solutions make them unsuitable for remote embedded systems such as nano and micro-satellites, which usually have very limited hardware capability and very strict limits on power consumption. In this paper, we present a CNN architecture for semantically segmenting SAR images into multiple classes. The proposed CNN is specifically designed to run on remote embedded systems, which have very limited hardware capability and strict limits on power consumption. Even if the performance in terms of results accuracy does not represent a step forward compared with previous solutions, the presented CNN has the important advantage of being able to run on remote embedded systems with limited hardware resources while achieving good performance. The presented CNN is compatible with dedicated hardware accelerators available on the market due to its low memory footprint and small size. It also provides many additional very significant advantages, such as having shorter inference times, requiring shorter training times, and avoiding transmission of irrelevant data. Our goal is to allow embedded low power remote devices such as satellite systems for remote sensing to be able to directly run CNNs on board, so that the amount of data that needs to be transmitted to ground and processed on ground can be substantially reduced, which will be greatly beneficial in significantly reducing the amount of time needed for identification of oil spills from SAR images.

Water column poly-aromatic hydrocarbon anomalies measured with submersible gliders in the Angolan natural oil seepage province

Fluorescence sensors mounted on unmanned underwater gliders open new ways of investigation to detect dissolved hydrocarbons in seawater. A glider was deployed for 20 days to monitor biogeochemical and physical signals associated with natural hydrocarbon seepages within the first 700 m in the Angolan waters. The glider was equipped with fluorometers (MiniFluo-UV) to measure the concentrations of polycyclic aromatic hydrocarbons (PAH) of interest, i.e. naphthalene, phenanthrene, fluorene and pyrene. A continuous PAH-like signal detected within the 70 m layer below the sea surface is associated with high chlorophyll concentration in the deep chlorophyll maximum. Vertical PAH-like anomalies forming either strong spikes or diffuse columns down to 700 m are observed at the exact location of oil seep sites identified on Synthetic Aperture Radar satellite images. An ~200 m thick layer of enhanced PAH-like concentration, topped by a thermo/pycnocline identified at 280–300 m water depth, is measured in concomitance with the decrease in oxygen concentration. The concomitance of these signals suggests that lower oxygen concentrations induce a preservation of hydrocarbons within the eastern Atlantic oxygen minimum zone. Even if the absence of in-situ measurements limits the understanding of physical and biogeochemical processes affecting PAH concentrations, the measurements conducted at the edges of the OMZ suggests a relationship with microbial activity and organic matter dynamics in this layer. The results presented here show that gliders equipped with PAH sensors represent a promising means for monitoring hydrocarbons in the oceans, especially when they are coupled with other systems (i.e. Synthetic Aperture Radar).

How do Sentinel-1 SAR images match with the existing maps

Synthetic Aperture Radar (SAR) images are used in several different applications for Remote Sensing purposes. SAR is an imaging sensor that can detect high-resolution ground images under a wide variety of imaging conditions. As SAR is an active system, the data are already acquired with geo-position information. To investigate to verify the image spatial accuracy, a part of the Antalya region of Turkey was selected as test site. The Open Street Map (OSM) and Photogrammetric Digital Map (PDM) data of the area on the Antalya O25 map were used in comparison. First, characteristic common points were selected on the OSM data and the SAR satellite image both. The projected coordinates of these points were calculated with the QGIS software. Normal distribution of the coordinate differences in these data sets were plotted. It was confirmed that the data sets were in normal distribution and standard deviation and 2 * standard deviation values were calculated. The maximum and minimum confidence interval (95%) was determined according to the standard deviation limit values. X and Y coordinate differences were calculated for 49 selected points from both image pairs SAR&OSM and SAR&PDM. Finally, the maximum differences show that the SAR positional accuracy respect to OSM and PSM is below 1 pixel azimuthal resolution.

Surface Wave Breaking Caused by Internal Solitary Waves: Effects on Radar Backscattering Measured by SAR and Radar Altimeter

Breaking surface waves play a key role in the exchange of momentum, heat, and gases between the atmosphere and the ocean. Waves break at the ocean’s surface at high or medium wind speeds or in the absence of wind due to shoaling of the seafloor. However, surface waves also break due to interactions with internal solitary waves (ISWs). In this paper, we revisit surface wave breaking caused by ISWs and how ISWs are manifested in synthetic aperture radar (SAR) images acquired by the TerraSAR-X and Sentinel-1 satellites and in high-resolution radar altimeter data acquired by the SAR altimeter (SRAL) onboard the Sentinel-3A satellite. X-band TerraSAR-X images acquired at low wind speeds suggest that meter-scale surface breaking waves resulting from large-scale ISWs are associated with large modulations in backscatter at HH and VV polarizations that cannot be explained by present theories. Furthermore, Sentinel-1 C-band SAR satellite images acquired at moderate to high wind speeds also exhibit large radar signatures from surface wave breaking at VV and VH cross-polarizations. Finally, new observations from the Sentinel-3 SRAL altimeter show clear evidence of significant wave height (SWH) variations along the propagation paths of ISWs. The SWH signatures are unique in showing that the surface wave energy does not return to its unperturbed level after an ISW passes, most likely because intense meter-scale wave breaking results in surface wave energy dissipation. In summary, these results show that surface wave breaking contributes significantly to radar remote sensing of ISWs.

Multithresholding techniques in SAR image classification

Beyond bilevel segmentation, multithresholding (MT) is a powerful technique that is seldom considered in image analysis. In active imaging techniques, however, MT appears as a significant alternative due to its obliviousness to the inherent nonlinear noise present in these kind of images. This is especially the case in satellite synthetic aperture radar (SAR) images, which are becoming an increasingly popular information source in remote sensing, given that the acquisition is independent of weather and daylight conditions. In SAR images, data-dependent multiplicative noise (speckle) hampers most of the image filtering techniques available from linear theory, and thus nonlinear techniques like MT appear to be promising. There are, however, a large amount of proposals in this direction, each with different theoretical or empirical justifications, and thus a careful analysis of their advantages in results quality and computational cost have to be assessed. In this paper we survey the most representative MT techniques and methods applied to SAR imagery (both synthetic and actual satellite images), and evaluate them in terms of region segmentation quality and computational cost. Results show that the maximum likelihood method provides the best quality segmentation results at the expense of higher computation times, while a state transition based method provides the fastest results with acceptable quality, and that all methods can be assessed with a simple tradeoff representation.

A High-Resolution Flood Inundation Archive (2016–the Present) from Sentinel-1 SAR Imagery over CONUS

AbstractMost existing inundation inventories are based on surveys, news, or passive remote sensing imagery. Affected by spatiotemporal resolution or weather conditions, these inventories are limited in spatial details or coverage. Satellite synthetic aperture radar (SAR) data have recently enabled flood mapping at unprecedented spatiotemporal resolution. However, the bottleneck in producing SAR-based flood maps is the requirement of expert manual processing to maintain acceptable accuracy by most SAR-driven mapping techniques. To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system. RAPID uses U.S. Geological Survey (USGS) water watch system and accumulated precipitation to identify SAR images that potentially overlap a flood event. The dataset include inundation events with the temporal scale from several days to months. Concluded from all 559 overlapping images in the period from 2016 to the first half of 2019, the comparison of the proposed dataset against the USGS Dynamic Surface Water Extent (DSWE) product yields an overall, user, producer agreements, and critical success index of 99.06%, 87.63%, 91.76%, and 81.23%, respectively, demonstrating the high accuracy of the proposed dataset and the robustness of the automated system. We anticipate this archive to facilitate many applications, including large-scale flood loss and risk assessment, and inundation model calibration and validation.

Flood modeling of a large transboundary river using WRF-Hydro and microwave remote sensing

The Brahmaputra River, a trans-boundary river between India and Bangladesh experiences large scale floods with frequent inundation events affecting millions of people annually. An integrated approach for quantitative understanding of flood wave characteristics and real time flood modeling framework can be helpful for effective decision making and planning of flood risk management strategies. In this study, we have characterized the flood wave propagation in the Brahmaputra River using gauge observations and model simulations. The spatio-temporal variation of flood lift is estimated using stage hydrograph of different gauge stations along the river. The temporal variation in flood hydrograph is characterized by different flood wave parameters for a gauge station which govern the flood condition in the Brahmaputra River. These flood wave characteristics are used in the categorization of different intensity of flood events. To estimate the river discharge, the WRF-Hydro model was set-up over the Brahmaputra river basin for hourly discharge estimation. The satellite observations form altimeter and Synthetic Aperture Radar (SAR) are used to estimate river flood-plain elevation and inundation. Altimetry data from Jason-3 satellite was used for water level retrieval and compared with gauge and simulated discharge time-series for further validation of model capabilities. Sentinel-1 satellite SAR images are used to assess the flood inundation extent and probability based on flood wave characteristics in the downstream flood-plain region of Guwahati. Finally, we have integrated these data-sets for a flood modeling and forecasting framework for a braided transboundary river system. This integrated system is studied in detail for an extreme event that occurred during July 2019 in the Brahmaputra River. An hourly estimate of each event for the flood period proved the capability of this system for operational deployment in near real-time flood assessment and forecasting purposes for the flood-prone region of South Asia.

Mapping of wheat lodging susceptibility with synthetic aperture radar data

Crop lodging reduces yield quantity and grain quality of cereal crops. Understanding seasonal variation in crop lodging susceptibility enables lodging risk assessments and predictions of associated crop yield losses. We demonstrate a novel remote sensing-based approach, using sparse field observations and widely available synthetic aperture radar (SAR) satellite imagery, to map a safety factor against root lodging (SFA) in wheat. SFA quantifies the ability of the rooting system to support the self-weight moment of the whole plant and can be used as an indicator of in-season root lodging susceptibility. SAR satellite images, from Sentinel-1 and RADARSAT-2, were acquired synchronously with field measurements in Jolanda di Savoia, Ferrara, Italy during the 2018 winter wheat growing season. The field data included measurements from non-lodged (healthy) wheat such as plant height, height at the centre of gravity, self-weight moment of the whole plant, soil anchorage strength, SFA and those from lodged wheat such as crop angle of inclination, lodged area and the point of plant failure (stem or root). Field measurements confirmed that SFA decreased progressively through the season and was consistent with the observed lodging. Strong and significant correlations through the season were observed between SFA and SAR satellite image metrics. The validated regression models showed a strong relationship between field-measured SFA and the metrics from RADARSAT-2 (R2CV = 0.84, RMSECV = 0.54) and Sentinel-1 data (R2CV = 0.73, RMSECV = 0.59). However, it is possible that the relationship between SFA and SAR parameters is confounded by the variations in crop biomass, which is highly correlated with SFA (r = −0.71, p < 0.0001). Our study, for the first time, demonstrates the use of remote sensing SAR data for lodging susceptibility assessment. Current and planned satellite platforms have the potential for large scale, operational assessment of lodging susceptibility in cereal crops.

Adaptive speckle filtering for real-time computing in low earth orbit satellite synthetic aperture radar

As the number of low Earth orbit satellites is increasing, new algorithms and high-performance computing resources are needed to handle the enormous real-time arrival data generated by satellites. Based on the requirements, this paper proposes a novel real-time adaptive speckle filtering selection algorithm for satellite synthetic aperture radar (SAR) images. The use of high-performance filters generates high-quality images whereas it introduces delays. Thus, a real-time adaptive algorithm which achieves time-average SAR image quality maximization subject to delays using Lyapunov optimization, where the delay is formulated with a queuing model. Evaluation results show that the proposed algorithm guarantees desired performance improvements.

Efficient Generation of Artificial Training DB for Ship Detection Using Satellite SAR Images

In this paper, we propose an effective scheme to generate artificial training database (DB) to mitigate the deficiency in the amount of training DB for ship detection using satellite synthetic aperture radar (SAR) images. In the proposed scheme, SAR signatures of ship targets are first obtained from two different types of image: 1) KOMPSAT-5 images with automatic identification system (AIS) information and 2) simulated SAR images which are acquired using electromagnetic numerical analysis (EMNA). Then, the ship signatures are combined with sea clutter models to generate realistic SAR patches. For overall ship detection tasks (i.e., training and testing), the oriented bounding box (OBB) concept was adopted to appropriately represent slender ship targets in SAR images. When compared with the rash collection of a large number of real SAR images, the proposed scheme is more efficient in terms of cost and time. Experimental results demonstrated that the proposed method can considerably aid in increasing the ship detection capacity of deep learning models. Thus, it is expected that the proposed scheme can be usefully exploited to solve the lack of the amount of training DB.