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.

A deep-learning based detector for M-ary phase position shift keying (MPPSK) systems is proposed in this paper. The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features from the modulation signals, and a softmax classifier. The features learned by the stacked DSAE were then used to train the softmax classifier to demodulate the received signals into M classes. The architecture presented herein was trained and tested on a simple dataset extended by adding Gaussian noise only. The results from the theoretical analysis and simulation show that the detection performance of the proposed scheme is superior to that of existing detectors.

Adaptive multilooking is a critical processing step in multitemporal interferometric synthetic aperture radar (InSAR) measurement, especially in small temporal baseline subsets. Various amplitude-based adaptive multilook approaches have been proposed for the improvement of interferometric processing. However, the phase signal, which is fundamental in interferometric systems, is typically ignored in these methods. To fully exploit the information in complex SAR images, a nonlocal adaptive multilooking is proposed based on complex covariance matrix in this work. The complex signal is here exploited for the similiarity measurement between two pixels. Given the complexity of objects in SAR images, structure feature detection is introduced to adaptively estimate covariance matrix. The effectiveness and reliability of the proposed approach are demonstrated with experiments both on simulated and real data.

Focusing highly squinted synthetic aperture radar (SAR) data of a short-range wide-swath (SRWS) region is a challenging task because the reference range linear range cell migration correction (LRCMC) and the inherent range-dependent squint angle produce two-dimensional (2-D) spatial-variant RCM and azimuth-dependent Doppler parameters. In this paper, a two-step imaging algorithm (strategy) based on a novel equidistant sphere model (ESM) is proposed to accommodate these issues. The ESM, which is used for depicting the spatial-variant property of the echo data, is established by an investigation into the property of the origin range after the keystone transform (KT) operation and the reference range RCMC. Based on the ESM, an azimuth-dependent high-order RCMC (ADH-RCMC) method is adopted in the first step to implement the residual azimuth-dependent RCMC. In the second step, a frequency extended nonlinear chirp scaling (FENLCS) algorithm is introduced to achieve the highly varying residual Doppler centroid correction and the azimuth-dependent high-order Doppler parameter equalization. Simulation results in the case of SRWS SAR demonstrate the superior performance of the proposed algorithm.INDEX TERMS synthetic aperture radar (SAR), short-range wide-swath (SRWS), equidistant sphere model (ESM), azimuth-dependent, frequency extended nonlinear chirp scaling (FENLCS).

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