Recently, the efficient solvers for compressive sensing (CS) problems with Total Variation (TV) regularization are needed, mainly because of the reconstruction of an image by a single pixel camera, or the recovery of a medical image from its partial Fourier samples. In this paper, we propose an alternating directions scheme algorithm for solving the TV regularized minimization problems with linear constraints. We minimize the corresponding augmented Lagrangian function alternatively at each step. Both of the resulting subproblems admit explicit solutions by applying a linear-time shrinkage. The algorithm is easily performed, in which, only two matrix-vector multiplications and two fast Fourier transforms are involved at per-iteration. The global convergence of the proposed algorithm follows directly in this literature. Numerical comparisons with the sate-of-theart method TVLA3 illustrate that the proposed method is effective and promising.

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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.

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Abstract. The Wuhan mesosphere–stratosphere–troposphere (MST) radar is a 53.8 MHz monostatic Doppler radar, located in Chongyang, Hubei Province, China, and has the capability to observe the dynamics of the mesosphere–stratosphere–troposphere region in the subtropical latitudes. The radar system has an antenna array of 576 Yagi antennas, and the maximum peak power is 172 kW. The Wuhan MST radar is efficient and cost-effective and employs more simplified and more flexible architecture. It includes 24 big transmitter–receiver (TR) modules, and the row or column data port of each big TR module connects 24 small TR modules via the corresponding row or column feeding network. Each antenna is driven by a small TR module with peak output power of 300 W. The arrangement of the antenna field, the functions of the timing signals, the structure of the TR modules, and the clutter suppression procedure are described in detail in this paper. We compared the MST radar observation results with other instruments and related models in the whole MST region for validation. Firstly, we made a comparison of the horizontal winds in the troposphere and low stratosphere observed by the Wuhan MST radar with the radiosonde on 22 May 2016, as well as with the ERA-Interim data sets (2016 and 2017) in the long term. Then, we made a comparison of the observed horizontal winds in the mesosphere with the meteor radar and the Horizontal Wind Model 14 (HWM-14) model in the same way. In general, good agreements can be obtained, and this indicates that the Wuhan MST is an effective tool to measure the three-dimensional wind fields of the MST region.

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