Search Results (9)

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

Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China’s Chang’e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data can hardly detect surface rocks because of median-averaging. High-resolution images from a synthetic aperture radar (SAR) can be used to map discrete rocks and small craters according to their strong backscattering. This study utilizes the You Only Look Once version 7 (YOLOv7) tool to detect varying-sized craters in SAR images. It also employs the Markov random field (MRF) algorithm to identify surface rocks, which are usually difficult to detect in DEM data. The results are validated by optical images and DEM data in non-PSR. With the assistance of the DEM data, regions with slopes larger than 10° are excluded. YOLOv7 and MRF are applied to detect craters and rocky surfaces and exclude regions with steep slopes in the PSRs of craters Shoemaker, Slater, and Shackleton, respectively. This study proves SAR images are feasible in the selection of landing sites in the PSRs for future missions. Full article

Aiming at SAR imaging for large coastal scenes, a comprehensive comparative study is performed based on Sentinel-1 raw data, SAR imaging simulation, and Google Maps. A parallel Range-Doppler (RD) algorithm is developed and applied to focus Sentinel-1 raw data for large coastal scenes, and the focused SAR image is compared with the multi-look-processed SAR image using SNAP 9.0.0 software, as well as the corresponding areas of Google Maps. A scheme is proposed to convert the LiDAR point cloud data of the coast into a 3D coastal area digital elevation model (DEM), and a tailored 3D model suitable for RaySAR simulator is obtained after statistical outlier removal (SOR) denoising and down-sampling processing. Comparison results show good agreements, which verify the effectiveness of the parallel RD algorithm as well as the backward ray-tracing-based RaySAR simulator, which serves as a powerful SAR imaging tool due to its high efficiency and flexibility. The cosine similarity between the RaySAR-simulated SAR image and Google Maps achieves 0.93, while cosine similarity reaches 0.85 between Sentinel-1 SAR-focused images with our parallel RD algorithm and multi-look SAR image processed using SNAP software. This article can provide valuable assistance for SAR system performance evaluation, SAR imaging algorithm improvement, and remote sensing applications. Full article

: We developed a GNSS-assisted InSAR phase unwrapping algorithm for large-deformation DInSAR data processing in coal mining areas. Utilizing the Markov random field (MRF) theory and simulated annealing, the algorithm derived the energy function using MRF theory, Gibbs distribution, and the Hammersley–Clifford theorem. It calculated an image probability ratio and unwrapped the phase through iterative calculations of the initial integer perimeter matrix, interference phase, and weight matrix. Algorithm reliability was confirmed by combining simulated phases with digital elevation model (DEM) data for deconvolution calculations, showing good agreement with real phase-value results (median error: 4.8 × 10⁻⁴). Applied to ALOS-2 data in the Jinfeng mining area, the algorithm transformed interferometric phase into deformation, obtaining simulated deformation by fitting GNSS monitoring data. It effectively solved meter-scale deformation variables between single-period images, particularly for unwrapping problems due to decoherence. To improve calculation speed, a coherence-based threshold was set. Points with high coherence avoided iterative optimization, while points below the threshold underwent iterative optimization (coherence threshold: 0.32). The algorithm achieved a median error of 30.29 mm and a relative error of 2.5% compared to GNSS fitting results, meeting accuracy requirements for mining subsidence monitoring in large mining areas. Full article

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%. Full article

Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes. Full article

Ground-based arc-scanning synthetic aperture radar (ArcSAR) is the novel ground-based synthetic aperture radar (GBSAR). It scans 360-degree surrounding scenes by the antenna attached to rotating boom. Therefore, compared with linear scanning GBSAR, ArcSAR has larger field of view. Although the feasibility of ArcSAR has been verified in recent years, its imaging algorithm still presents difficulties. The imaging accuracy of ArcSAR is affected by terrain fluctuation. For rotating scanning ArcSAR, even if targets in scenes have the same range and Doppler with antenna, if the heights of targets are different, their range migration will be different. Traditional ArcSAR imaging algorithms achieve imaging on reference plane. The height difference between reference plane and target in scenes will cause the decrease of imaging quality or even image defocusing because the range migration cannot be compensated correctly. For obtaining high-precision ArcSAR image, we propose interferometric DEM (digital elevation model)-assisted high precision imaging method for ArcSAR. The interferometric ArcSAR is utilized to acquire DEM. With the assist of DEM, target in scenes can be imaged on its actual height. In this paper, we analyze the error caused by ArcSAR imaging on reference plane. The method of extracting DEM on ground range for assisted ArcSAR imaging is also given. Besides, DEM accuracy and deformation monitoring accuracy of proposed method are analyzed. The effectiveness of the proposed method was verified by experiments. Full article

Knowledge of the aboveground biomass (AGB) of large pasture fields is invaluable as it assists graziers to set stocking rate. In this preliminary evaluation, we investigated the response of Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to biophysical variables (leaf area index, height and AGB) for native pasture grasses on a hilly, pastoral farm. The S1 polarimetric parameters such as backscattering coefficients, scattering entropy, scattering anisotropy, and mean scattering angle were regressed against the widely used morphological parameters of leaf area index (LAI) and height, as well as AGB of pasture grasses. We found S1 data to be more responsive to the pasture parameters when using a 1 m digital elevation model (DEM) to orthorectify the SAR image than when we employed the often-used Shuttle Radar Topography 30 m and 90 m Missions. With the 1m DEM analysis, a significant quadratic relationship was observed between AGB and VH cross-polarisation (R² = 0.71), and significant exponential relationships between polarimetric entropy and LAI and AGB (R² = 0.53 and 0.45, respectively). Similarly, the mean scattering angle showed a significant exponential relationship with LAI and AGB (R² = 0.58 and R² = 0.83, respectively). The study also found a significant quadratic relationship between the mean scattering angle and pasture height (R² = 0.72). Despite a relatively small dataset and single season, the mean scattering angle in conjunction with a generalised additive model (GAM) explained 73% of variance in the AGB estimates. The GAM model estimated AGB with a root mean square error of 392 kg/ha over a range in pasture AGB of 443 kg/ha to 2642 kg/ha with pasture LAI ranging from 0.27 to 1.87 and height 3.25 cm to 13.75 cm. These performance metrics, while indicative at best owing to the limited datasets used, are nonetheless encouraging in terms of the application of S1 data to evaluating pasture parameters under conditions which may preclude use of traditional optical remote sensing systems. Full article

The Gaofen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) with the ability of high-accuracy mapping in China. However, the Ground Control Points (GCPs) are essential to ensure the accuracy of mapping for GF-3 SAR imagery at present. In this paper, we analyze the error sources that affect the geometric processing and propose a new block adjustment method without GCPs for GF-3 SAR imagery. Firstly, the geometric calibration of GF-3 image is carried out. Secondly, the rational polynomial coefficient (RPC) model is directly generated after the geometric calibration parameters compensation of each image. Finally, we solve the orientation parameters of the GF-3 images through DEM assisted planar block adjustment and conduct ortho-rectification. With two different imaging modes of GF-3 satellite, which include the QPSI and FS2, we carry out the block adjustment without GCPs. Experimental results of testing areas including Wuhan city and Hubei province in China show that the geometric mosaic accuracy and the absolute positioning accuracy of the orthophoto are better than one pixel, which has laid a good foundation for the application of GF-3 image in global high-accuracy mapping. Full article