Search Results (74)

Dense 3D reconstruction from multi-aspect angle synthetic aperture radar (SAR) imagery has gained considerable attention for urban monitoring applications. However, achieving reliable dense matching between multi-aspect SAR images remains challenging due to three fundamental issues: anisotropic scattering characteristics that cause inconsistent features across different aspect angles, geometric distortions, and speckle noise. To overcome these limitations, we introduce SAR-DAISY, a novel local feature descriptor specifically designed for dense matching in multi-aspect SAR images. The proposed method adapts the DAISY descriptor structure to SAR images specifically by incorporating the Gradient by Ratio (GR) operator for robust gradient calculation in speckle-affected imagery and enforcing multi-aspect consistency constraints during matching. We validated our method on W-band airborne SAR data collected over urban areas using circular flight paths. Experimental results demonstrate that SAR-DAISY generates detailed 3D point clouds with well-preserved structural features and high computational efficiency. The estimated heights of urban structures align with ground truth measurements. This approach enables 3D representation of complex urban environments from multi-aspect SAR data without requiring prior knowledge. Full article

Multi-aspect synthetic aperture radar (SAR) is an emerging observation mode in SAR. Through multi-aspect observations, along with coherent and incoherent image processing, multi-aspect SAR effectively addresses issues like layovers, shadows, and foreshortening in conventional SAR. It can obtain multi-aspect scattering images of the observed scene. Modeling the statistical distribution characteristics of multi-aspect SAR images is crucial for its processing and applications. Currently, there is no comprehensive and systematic study on the statistical distribution characteristics of multi-aspect SAR images. Therefore, this paper conducts qualitative and quantitative analyses of these characteristics. Furthermore, we investigate the applicability and limitations of five single-parametric models commonly used in conventional SAR for modeling the statistical distribution characteristics of multi-aspect SAR images. The experimental results show that none of these models could accurately model the multi-aspect SAR images. To address this issue, we propose a finite mixture model (FMM) and evaluate its feasibility to accurately model the statistical distribution characteristics of multi-aspect SAR on X-band GOTCHA data and C-band Zhuhai data. The experimental results demonstrate that, compared with the single-parametric models, our method can accurately model the statistical distribution characteristics of various types of targets in multi-aspect SAR images from different observation aspects and aperture angles in various bands. Full article

Target detection in synthetic aperture radar (SAR) imagery remains a significant technical challenge, particularly in scenarios involving multi-target interference and clutter edge effects that cannot be disregarded, notably in high-resolution imaging applications. To tackle this issue, a novel two-stage superpixel-level constant false-alarm rate (CFAR) detection method based on a truncated kernel density estimation (KDE) model is proposed in this article. The contribution mainly lies in three aspects. First, a truncated KDE model is used to fit the statistical distribution of clutter in the detection window, and adaptive thresholding is used for clutter truncation to remove outliers from the clutter samples while preserving the real clutter. Second, based on the clutter statistics, the KDE model is accurately constructed using the quartile based on the truncated clutter statistics. Third, target superpixel detection is performed using a two-stage CFAR detection scheme enhanced with local contrast measure (LCM), consisting of a global stage followed by a local stage. In the global detection phase, we identify candidate target superpixels (CTSs) based on the superpixel segmentation results. In the local detection phase, a local CFAR detector using a truncated KDE model is employed to improve the detection process, and further screening is performed on the global detection results combined with local contrast. Experimental results show that the proposed method achieves excellent detection performance, while significantly reducing detection time compared to current popular methods. Full article

Due to the huge differences in Synthetic Aperture Radar (SAR) image features of the same target under different observing aspects, the demand for constructing multi-aspect SAR datasets of various typical targets is becoming increasingly urgent with the expansion of SAR technology application fields. Meanwhile, multi-aspect interpolation techniques for constructing multi-aspect SAR datasets, based on electromagnetic scattering features and on Generative Adversarial Networks (GANs), have some shortcomings that are difficult to address. The former method provide descriptions of the target scattering so overly idealized that they are not real, while the latter method suffers from incomplete amplitude information and a loss of phase information in multi-aspect interpolation results due to the SAR images input into GANs being phaseless and amplitude-quantized. In response to the above issues, this paper proposes the Multi-aspect Scattering Information Complex GAN (MS-CGAN) guided by the scattering information in observing aspects of SAR images to simulate the multi-aspect interpolation of SAR images from specific aspects. MS-CGAN provides a new approach for dataset construction and augmentation. Moreover, as a complex network, MS-CGAN does not require phase removal or amplitude quantization of the input SAR images; thus, the significant issue of the severe loss of scattering information in multi-aspect interpolation methods based on GANs is greatly addressed. In the experiments, assuming the absence of real SAR images from certain aspects, both the correlation coefficient and the phase correlation between interpolated SAR images from MS-CGAN and real SAR images achieve good results. In the case of a sampling aspect interval of 10°, the mean correlation of the amplitude and phase of the interpolated SAR images and the corresponding real SAR images both reach over 80%. In the case of a sampling aspect interval of 20°, the mean correlation of the amplitude and phase of the interpolated SAR images and the corresponding real SAR images remain above 75%. In the case of a sampling aspect interval of 30°, the mean correlation of the amplitude and phase of the interpolated SAR images and the corresponding real SAR images can reach around 70%. Energy integration curves are completed at specific aspects, demonstrating the effectiveness of the MS-CGAN multi-aspect interpolation method. Full article

Floods have significant impacts on economic development and cause the loss of both lives and property, posing a serious threat to social stability. Effectively identifying the evolution patterns of floods could enhance the role of flood monitoring in disaster prevention and mitigation. Firstly, in this study, we utilized low-cost multi-source multi-temporal remote sensing to construct an HIS-NSCT fusion model based on SAR and optical remote sensing in order to obtain the best fusion image. Secondly, we constructed a regional growth model to accurately identify floods. Finally, we extracted and analyzed the extent, depth, and area of the farmland submerged by the flood. The results indicated that the HIS-NSCT fusion model maintained the spatial characteristics and spectral information of the remote sensing images well, as determined through subjective and objective multi-index evaluations. Moreover, the regional growth model could preserve the detailed features of water body edges, eliminate misclassifications caused by terrain shadows, and enable the effective extraction of water bodies. Based on multi-temporal remote sensing fusion images of Poyang Lake, and incorporating precipitation, elevation, cultivated land, and other data, the accurate identification of the flood inundation range, inundation depth, and inundated cultivated land area can be achieved. This study provides data and technical support for regional flood identification, flood control, and disaster relief decision-making, among other aspects. Full article

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately −16 mm/a over the 22 km section of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 °C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze–thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone. Full article

This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1, and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. These data cover 45 countries with a total of 31,730 wildfire events from 2018 to 2022. All of these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test the effectiveness of different deep learning architectures in predicting the size and shape of wildfire-burned areas. This study incorporates both image segmentation networks and visual transformers, employing a consistent experimental design across various models to ensure the comparability of the results. Adjustments were made to the training data, such as the exclusion of empty labels and very small events, to refine the focus on more significant wildfire events and potentially improve prediction accuracy. The models’ performance was evaluated using metrics like F1 score, IoU score, and Average Percentage Difference (aPD). These metrics offer a multi-faceted view of model performance, assessing aspects such as precision, sensitivity, and the accuracy of the burned area estimation. Through extensive testing the final model utilizing LinkNet and ResNet-34 as backbones, we obtained the following metric results on the test set: 0.86 F1 score, 0.75 IoU, and 70% aPD. These results were obtained when all of the available samples were used. When the empty labels were absent during the training and testing, the model increased its performance significantly: 0.87 F1 score, 0.77 IoU, and 44.8% aPD. This indicates that the number of samples, as well as their respectively size (area), tend to have an impact on the model’s robustness. This restriction is well known in the remote sensing domain, as accessible, accurately labeled data may be limited. Visual transformers like TeleViT showed potential but underperformed compared to segmentation networks in terms of F1 and IoU scores. Full article

In this work, the airborne Synthetic Aperture Radar (SAR) infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) is described. This infrastructure allows IREA-CNR to plan and execute airborne SAR campaigns and to process the acquired data with a twofold aim. On one hand, the aim is to develop research activities; on the other hand, the aim is to support the emergency prevention and management activities of the Department of Civil Protection of the Italian Presidency of the Council of Ministers, for which IREA-CNR serves as National Centre of Competence. Such infrastructure consists of a flight segment and a ground segment that include a multi-frequency airborne SAR sensor based on the Frequency-Modulated Continuous Wave (FMCW) technology and operating in the X- and L-bands, an Information Technology (IT) platform for data storage and processing and an airborne SAR data processing chain. In this work, the technical aspects related to the flight and ground segments of the infrastructure are presented. Moreover, a discussion on the response times and characteristics of the final products that can be achieved with the infrastructure is provided with the aim of showing its capabilities to support the monitoring activities required in a possible emergency scenario. In particular, as a case study, the acquisition and subsequent interferometric processing of airborne SAR data relevant to the Stromboli volcanic area in the Sicily region, southern Italy, are presented Full article

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. Full article

Le’an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town’s terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le’an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le’an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value. Full article

In order to address the problems of range ambiguity and azimuth ambiguity in the wide-swath imaging of synthetic aperture radar (SAR), this paper proposes a multi-beam SAR two-dimensional ambiguity suppression method based on azimuth phase coding (APC). The scheme employs an elevation simultaneous multi-beam transmission system with azimuth under-sampling, transmitting different APC waveforms to various range-ambiguous sub-regions. After receiving the echoes, the azimuth digital beamforming (DBF) is used to separate the APC waveform echoes with multi-order Doppler ambiguity, achieving azimuth reconstruction and range ambiguity suppression simultaneously. Finally, the elevation nulling DBF is used to further suppress range ambiguity and obtain the SAR wide-swath image. The superiority of this scheme is reflected in the following aspects: the azimuth DBF simultaneously suppresses azimuth and range ambiguity, the influence of height fluctuations on the ability to suppress range ambiguity is weakened, the use of elevation nulling DBF further enhances the level of range ambiguity suppression, and different range sub-regions can adopt different range resolutions and working modes. The feasibility of this scheme is verified through theoretical analysis and simulation. Full article

Accurate detection of sea ice leads is essential for safe navigation in polar regions. In this paper, a shape-aware (SA) network, SA-DeepLabv3+, is proposed for automatic lead detection from synthetic aperture radar (SAR) images. Considering the fact that training data are limited in the task of lead detection, we construct a dataset fusing dual-polarized (HH, HV) SAR images from the C-band Sentinel-1 satellite. Taking the DeepLabv3+ as the baseline network, we introduce a shape-aware module (SAM) to combine multi-scale semantic features and shape information and, therefore, better capture the shape characteristics of leads. A squeeze-and-excitation channel-position attention module (SECPAM) is designed to enhance lead feature extraction. Segmentation loss generated by the segmentation network and shape loss generated by the shape-aware stream are combined to optimize the network during training. Postprocessing is performed to filter out segmentation errors based on the aspect ratio of leads. Experimental results show that the proposed method outperforms the existing benchmarking deep learning methods, reaching 96.82% for overall accuracy, 93.01% for F1-score, and 91.48% for mIoU. It is also found that the fusion of dual-polarimetric SAR channels as the input could effectively improve the accuracy of sea ice lead detection. Full article

Ground deformation is one of the crucial issues threatening many cities in both societal and economic aspects. Interferometric synthetic aperture radar (InSAR) has been widely used for deformation monitoring. Recently, there has been an increasing availability of massive archives of SAR images from various satellites or sensors. This paper introduces Multi-Satellite SBAS that exploits complementary information from different SAR data to generate integrated long-term ground displacement time series. The proposed method is employed to create the vertical displacement maps of Almokattam City in Egypt from 2000 to 2020. The experimental results are promising using ERS, ENVISAT ASAR, and Sentinel-1A displacement integration. There is a remarkable deformation in the vertical direction along the west area while the mean deformation velocity is −2.32 mm/year. Cross-validation confirms that the root mean square error (RMSE) did not exceed 2.8 mm/year. In addition, the research findings are comparable to those of the previous research in the study area. Consequently, the proposed integration method has great potential to generate displacement time series based on multi-satellite SAR data; however, it still requires further evaluation using field measurements. Full article

The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system. Full article

Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination ($R^2$) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies. Full article