The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.

The combination of linear range walk correction and keystone transform is a good choice to focus high-resolution highly squint synthetic aperture radar (SAR) data because it is an effective way to remove linear range cell migration (RCM) completely and mitigate range-azimuth coupling. However, the results of this kind of imaging algorithm produce 2-D-variant residual RCM and variant-dependence Doppler phases. To obtain high-quality SAR image, an improved imaging algorithm using an azimuth-variant residual RCM correction (RCMC) and an extended nonlinear chirp scaling (ENLCS) is proposed in this letter. A new circle model is constructed to analyze the azimuth-variant properties of the residual high-order RCM and the Doppler phases. Based on this circle model, an azimuth-variant residual RCMC is implemented by multiplying a fourth-order phase function, and an improved ENLCS is derived to accomplish the azimuth equalization for azimuth compression. Simulation results validate the excellent performance of the proposed algorithm.

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

Distributed scatterer (DS) interferometric synthetic aperture can retrieve maximum available information by jointly processing persistent scatterers (PSs) and DSs. Unlike PSs, DSs are vulnerable to temporal, geometrical, and volumetric decorrelation. The phase optimization of DSs is essential for reliable parameter estimation. However, the preprocessing of DSs is very computationally expensive, and this drawback limits its engineering application to some degree. To improve computational efficiency, a fast scheme for reliable phase optimization of DSs is proposed based on the coherence-weighted model in this letter. The Gauss–Seidel iteration, having the advantages of fast convergence rate and small data memory, is introduced to solve the adopted phase optimization model. Experiments both on simulated data and real data are used to verify the reliability and efficiency of the presented method in this letter.

While improving the performance, nonblocking communication is prone to synchronization errors between MPI applications and the underlying MPI libraries. Such synchronization error occurs in the following way. After initiating nonblocking communication and performing overlapped computation, the MPI application reuses the message buffer before the MPI library completes the use of the same buffer, which may lead to sending out corrupted message data or reading undefined message data. This paper presents a new method called Sync Checker to detect synchronization errors in MPI nonblocking communication. To examine whether the use of message buffers is well synchronized between the MPI application and the MPI library, Sync Checker first tracks relevant memory accesses in the MPI application and corresponding message send/receive operations in the MPI library. Then it checks whether the correct execution order between the MPI application and the MPI library is enforced by the MPI completion check routines. If not, Sync Checker reports the error with diagnostic information. To reduce runtime overhead, we propose three dynamic optimizations. We have implemented a prototype of Sync Checker on Linux and evaluated it with seven bug cases, i.e., five introduced by the original developers and two injected, in four different MPI applications. Our experiments show that Sync Checker detects all the evaluated synchronization errors and provides helpful diagnostic information. Moreover, our experiments with seven NAS Parallel Benchmarks demonstrate that Sync Checker incurs moderate runtime overhead, 1.3-9.5 times with an average of 5.2 times, making it suitable for software testing.

Image formation for bistatic synthetic aperture radar data with nonparallel tracks is a challenging task due to the existence of the spatial variance of range cell migration (RCM) and azimuth frequency modulation (FM) rate. In this letter, an extended nonlinear chirp scaling (ENLCS) algorithm based on a quadratic ellipse model with two motion platform parameters is proposed for this configuration. For range processing, a method combining linear range walk correction and keystone transform is adopted to remove linear RCM completely and mitigate range–azimuth cross coupling. For azimuth focusing, a quadratic ellipse model using the two motion platform parameters is established to depict the azimuth-variant properties of the azimuth FM rate, and its accurate high-order approximation is derived as well. Investigations of the quadratic phase error show that great improvement can be made by this model. Following which, coefficients of the ENLCS are derived to accomplish the azimuth equalization. Simulation results validate the effectiveness of the algorithm proposed by this letter.