Search Results (279)

The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency. Full article

Due to the sparse distribution of ship targets in wide-area offshore scenarios, the typical cascade mode of imaging and detection for space-borne Synthetic Aperture Radar (SAR) echo data would consume substantial computational time and resources, severely affecting the timeliness of ship target information acquisition tasks. Therefore, we propose a ship target integrated imaging and detection framework (ST-IIDF) for SAR oceanic region data. A two-step filtering structure is added in the SAR imaging process to extract the potential areas of ship targets, which can accelerate the whole process. First, an improved peak-valley detection method based on one-dimensional scattering characteristics is used to locate the range gate units for ship targets. Second, a dynamic quantization method is applied to the imaged range gate units to further determine the azimuth region. Finally, a lightweight YOLO neural network is used to eliminate false alarm areas and obtain accurate positions of the ship targets. Through experiments on Hisea-1 and Pujiang-2 data, within sparse target scenes, the framework maintains over 90% accuracy in ship target detection, with an average processing speed increase of 35.95 times. The framework can be applied to ship target detection tasks with high timeliness requirements and provides an effective solution for real-time onboard processing. Full article

In recent years, synthetic aperture radars (SARs) have been widely applied in various fields due to their all-weather, day-and-night global imaging capabilities. As one of the most common types of antennas, the reflector antenna offers some advantages for spaceborne radars, including low cost, lightweight, high gain, high radiation efficiency, and low sidelobes. Consequently, spaceborne SAR systems based on reflector antennas exhibit significant potential. This paper reviews the main types and characteristics of reflector antennas, with particular attention to the structural configurations and feed arrangements of deployable reflector antennas in spaceborne SAR applications. Additionally, some emerging techniques, such as digital beamforming, staggered SAR, and SweepSAR based on reflector antennas, are examined. Finally, future development directions in this field are discussed, including high-resolution wide-swath imaging and advanced antenna deployment schemes. Full article

Image matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-scale adaptive improved SAR image block matching method (called STSU–SAR–SIFT). To improve accuracy, this method addresses the issue of the number of feature points under different thresholds by using the SAR–Shi–Tomasi response function in a multi-scale space. Then, the SUSAN function is used to constrain the effect of coherent noise on the initial feature points, and the multi-scale and multi-directional GLOH descriptor construction approach is used to boost the robustness of descriptors. To improve efficiency, the method adopts the main and additional image overlapping area matching method to reduce the search range and uses multi-core CPU+GPU collaborative parallel computing to boost the efficiency of the SAR–SIFT algorithm by block processing the overlapping area. The experimental results demonstrate that the STSU–SAR–SIFT approach presented in this paper has better accuracy and distribution. After the algorithm acceleration, the efficiency is obviously improved. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article

To satisfy the requirement of the modern spaceborne synthetic aperture radar (SAR) system, SAR imaging mode design makes a trade-off between resolution and swath coverage by controlling radar antenna sweeping. Existing spaceborne SAR systems can perform earth observation missions well in various modes, but they still face challenges in data acquisition, storage, and transmission, especially for high-resolution wide-swath imaging. In the past few years, sparse signal processing technology has been introduced into SAR to try to solve these problems. In addition, sparse SAR imaging shows huge potential to improve system performance, such as offering wider swath coverage and higher recovered image quality. In this paper, the design scheme of spaceborne sparse SAR imaging modes is systematically introduced. In the mode design, we first design the beam positions of the sparse mode based on the corresponding traditional mode. Then, the essential parameters are calculated for system performance analysis based on radar equations. Finally, a sparse SAR imaging method based on mixed-norm regularization is introduced to obtain a high-quality image of the considered scene from the data collected by the designed sparse modes. Compared with the traditional mode, the designed sparse mode only requires us to obtain a wider swath coverage by reducing the pulse repetition rate (PRF), without changing the existing on-board system hardware. At the same time, the reduction in PRF can significantly reduce the system data rate. The problem of the azimuth ambiguity signal ratio (AASR) increasing from antenna beam scanning can be effectively solved by using the mixed-norm regularization-based sparse SAR imaging method. Full article

Spaceborne high-resolution wide-area SAR image formation processing faces critical challenges induced by orbital curvature, Earth rotation, and spherical ground surfaces. The Spherical Geometry Algorithm (SGA) offers an effective solution to these problems. However, the standard SGA is inherently limited to spotlight mode SAR data processing and cannot be directly extended to other operational modes. To overcome this constraint, this paper proposes an enhanced SGA framework tailored for sliding spotlight mode SAR data processing. Firstly, this paper presents a rigorous analysis of time–frequency relationship variations during the classical SGA processing under sliding spotlight mode, and gives the reasons why the classical SGA can not be directly applied to the data processing in sliding spotlight mode. Then, a modified SGA processing framework is proposed to address the signal sampling ambiguity problem faced by the SGA in processing sliding spotlight mode data. The improved algorithm avoids the sampling ambiguity problem during azimuthal resampling and azimuthal IFFT by introducing an instantaneous Doppler central frequency correction processing before azimuthal resampling and a suitable amount of oversampling during azimuthal resampling. Finally, the effectiveness of the algorithm is verified by measured real data processing. Full article

Monitoring the amount of waste in open landfill sites in developing countries is important from the perspective of building a sustainable society and protecting the environment. Some landfill sites provide information on the amount of waste in reports and news articles; however, in many cases, the survey methods, timing, and accuracy are uncertain, and there are many sites for which this information is not available. In this context, monitoring the amount of waste using satellite data is extremely useful from the perspective of uniformity, objectivity, low cost, safety, wide coverage area, and simultaneity. In this study, we developed a method for calculating the relative volume of waste at 15 landfill sites in six developing countries using time-series digital surface model (DSM) data from the satellite optical sensor, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which has accumulated more than 20 years of observational data. Unnecessary variations between images were reduced by bias correction based on a reference area around the site. In addition, by utilizing various reported values, we introduced a method for converting relative volume to absolute volume and converting volume to weight, enabling a direct comparison with reported values. We also evaluated our method compared with the existing method for calculating changes in waste volume based on TanDEM-X DEM Change Map (DCM) products. The findings of this study demonstrated the efficacy of the employed method in capturing changes, such as increases and stagnation, in the amount of waste deposited. The method was found to be relatively consistent with reported values and those obtained using the DCM, though a decrease in accuracy was observed due to the depositional environment and the absence of data. The results of this study are expected to be used in the future for technology that combines an optical sensor and synthetic aperture radar (SAR) to monitor the amount of waste. Full article

Ship wake detection plays a crucial role in compensating for target detection failures caused by defocusing or displacement in SAR images due to vessel motion. This study addresses the challenge of enhancing wake features in high-resolution spaceborne SAR by exploiting the distinct linear characteristics of wake echoes and the random motion of ocean background clutter. We propose a novel method based on sub-aperture image sequences, which integrates equivalent multi-channel technology to fuse wake and wave information. This approach significantly improves the quality of raw wake images by enhancing linear features and suppressing background noise. The Radon transform is then applied to evaluate the enhanced wake images. Through a combination of principle analysis, enhancement processing, and both subjective and objective evaluations, we conducted experiments using real data from the AS01 SAR satellite and compared our method with traditional wake enhancement techniques. The results demonstrate that our method achieves significant wake enhancement and improves the recognition of detail wake features. Full article

To achieve on-board real-time processing for sliding-spotlight mode synthetic aperture radar (SAR), on the one hand, this paper proposes an adaptive and efficient imaging algorithm for the sliding-spotlight mode. On the other hand, a batch processing method was designed and optimized based on the AGX Orin platform to implement the algorithm effectively. Based on the chirp scaling (CS) algorithm, sliding-spotlight mode imaging can be achieved by adding Deramp preprocessing along with either zero-padding or performing an extra chirp scaling operation. This article analyzes the computational complexity of the two algorithms and provides a criterion called the Method Choice Indicator (MCI) for selecting the appropriate method. Additionally, the mathematical expressions for time–frequency transformation are derived, providing the theoretical basis for calculating the equivalent PRF and the azimuth width represented by a single pixel. To increase the size of the data that AGX Orin can process, the batch processing method was proposed to reduce peak memory usage during imaging, so that the limited memory could be better utilized. Meanwhile, this algorithm was also compatible with strip mode and TOPSAR (Terrain Observation by Progressive scans SAR) mode imaging. While batch processing increased data transfers, the integrated architecture of AGX Orin minimized the negative impact. Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. As a result, it took 19.25 s to complete the imaging process for sliding-spotlight mode data with a size of 42,966 × 27,648. Since satellite data acquisition time was 11.43 s, it can be considered that this method achieved near-real-time imaging. The experimental results demonstrate the feasibility of on-board processing. Full article

Based on Digital Beamforming (DBF) technology, spaceborne SAR systems can achieve high-resolution and wide-swath (HRWS) imaging. When combined with reflector antennas, the DBF-SCORE (Digital Beamforming-SCan On REceive) system also features light weight and low cost, making it an important choice for spaceborne HRWS SAR. This paper firstly proposes an advanced Full-chain Real-time Internal Calibration (FRIC) scheme, where the calibration path covers the entire receive chain from the antenna feed port to the input port of the Analog-to-Digital Converter (ADC) and achieves high-precision internal calibration concurrently with data acquisition. Secondly, based on the L-band reflector antenna DBF-SCORE system architecture, the design of radio frequency (RF) front end, namely the Transmit-Receive-Calibration Module (TRCM), is carried out. We propose the implementation of azimuth encoding modulation of the calibration signal through periodic switch control within the TRCM. Subsequently, the calibration signal is extracted using waveform diversity technology and its Signal-to-Noise Ratio (SNR) is improved through azimuth coherent integration technology. In addition, a ground verification system is established using the TRCM to evaluate the comprehensive performance of transmitting, receiving, and real-time internal calibration. Experimental results verify the effectiveness of the FRIC scheme and provide valuable insights for future spaceborne DBF SAR systems. Full article

Spaceborne synthetic aperture radar (SAR) has been proven capable of observing the directional ocean wave spectrum across the global ocean. Most of the efforts focus on the integrated wave parameters to characterize the imaged ocean wave properties. The newly proposed spectrum-based radar parameter mean cross-spectrum (MACS) is investigated using SAR image spectral properties of range-traveling waves at a wavelength of 20 m, based on Sentinel-1 wave mode acquisition of high spatial resolution (5 m). The magnitude of MACS is documented relative to environmental conditions (wind speed and direction) in terms of its variation for two polarizations at two incidence angles. This parameter exhibits distinct upwind–downwind asymmetry and polarization ratio at two incidence angles (23.8° and 36.8°). In addition, by comparing the SAR measurements with simulated MACS, we derive an improved real aperture radar modulation transfer function. Results obtained in this study shall help obtain a more accurate ocean wave spectrum based on the improved RAR modulations. Full article

With the development and application of spaceborne Synthetic Aperture Radar (SAR), higher resolution and a wider swath have become significant demands. However, as the resolution increases and the swath widens, the two-dimensional (2D) spatial variation between different targets in the scene and the radar becomes very pronounced, severely affecting the high-precision focusing and high-quality imaging of spaceborne SAR. In previous studies on the correction of two-dimensional spatial variation in spaceborne SAR, either the models were not accurate enough or the computational efficiency was low, limiting the application of corresponding algorithms. In this paper, we first establish a slant range model and a signal model based on the zero-Doppler moment according to the spaceborne SAR geometry, thereby significantly reducing the impact of azimuth spatial variation in two-dimensional spatial variation. Subsequently, we propose a Curve-Sphere Model (CUSM) to describe the ground observation geometry of spaceborne SAR, and based on this, we establish a more accurate theoretical model and quantitative description of two-dimensional spatial variation. Next, through modeling and simulation, we conduct an in-depth analysis of the impact of two-dimensional spatial variation on spaceborne SAR imaging, obtaining corresponding constraints and thresholds and concluding that in most cases, only one type of azimuth spatial variation needs to be considered, thereby greatly reducing the demand and difficulty of two-dimensional spatial variation correction. Relying on these, we propose a two-dimensional spatial variation correction method that combines range blocking and azimuth nonlinear chirp scaling processing and analyze its scalability to be applicable to more general cases. Finally, the effectiveness and applicability of the proposed method are validated through both simulation experiments and real data experiments. Full article

Spaceborne Synthetic Aperture Radar (SAR) is extensively used in maritime surveillance due to its ability to monitor vast oceanic regions regardless of weather conditions and sun illumination. Over the years, numerous automatic ship detection algorithms have been developed, utilizing either single-polarimetric data (i.e., intensity) or leveraging additional information provided by polarimetric sensors. One of the main challenges in automatic ship detection using SAR is that sea clutter, influenced primarily by sea conditions and image acquisition angles, can exhibit strong backscatter, reducing the signal-to-clutter ratio (that is, the contrast) between ships and their surroundings. This leads inevitably to detection errors, which can be either false alarms or miss-detections. A potential solution to this issue is to develop methodologies that suppress backscattered signals from the sea while preserving the radar returns from ships. In this work, we analyse a contrast enhancement method which is designed to suppress unwanted sea clutter while preserving signals from potential ships. A key advantage of this method is that it is fully analytical, eliminating the need for numerical optimization and enabling the rapid generation of an enhanced image better suited for automatic detection. This technique, based on polarimetric orthogonality, was originally formulated for quad-polarimetric data, and here the adaptation for dual-polarimetric SAR images is also detailed. To demonstrate its effectiveness, a comprehensive set of results using both quad- and dual-polarimetric images acquired by various sensors operating at L-, C-, and X-band is presented. Full article

SAR data from the lunar spaceborne Reconnaissance Orbiter’s (LRO) Mini-RF and Chandrayaan-1’s Mini-SAR provide valuable insights into the properties of the lunar surface. However, public lunar SAR data products are not properly registered and are limited by localization issues. Existing registration methods for Earth SAR have proven to be inadequate in their robustness for lunar data registration. And current research on methods for lunar SAR has not yet focused on producing globally registered datasets. To solve these problems, this article introduces a robust automatic registration method tailored for S-band Level-1 Mini-RF and Mini-SAR data with the assistance of lunar DEM. A simulated SAR image based on real lunar DEM data is first generated to assist the registration work, and then an offset calculation approach based on normalized cross-correlation (NCC) and specific processing, including background removal, is proposed to achieve the registration between the simulated image, and the real image. When applying Mini-RF images and Mini-SAR images, high robustness and good accuracy are exhibited, which produces fully registered datasets. After processing using the proposed method, the average error between Mini-RF images and DEM references was reduced from approximately 3000 m to about 100 m. To further explore the additional improvement of the proposed method, the registered lunar SAR datasets are used for further analysis, including a review of the circular polarization ratio (CPR) characteristics of anomalous craters. Full article