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It is known that a synthetic aperture radar (SAR) image obtained by matched filtering (MF)-based algorithms always suffers from serious noise, sidelobes, and clutters. However, the improvement of the image quality means the complexity of the SAR system will increase, which affects the application of the SAR image. The introduction of the sparse signal processing technique into SAR imaging proposes a new way to solve this problem. Sparse SAR image obtained by sparse recovery algorithms shows a better image performance than the typical complex SAR image with lower sidelobes and higher signal-to-noise ratio. As the most widely applied field of the SAR image, target classification relies on the SAR image with high quality. Therefore, a novel target classification model based on the amplitude and phase information of the sparse SAR image is introduced in this article. First, complex sparse image dataset is constructed by a novel iterative soft thresholding (BiIST) algorithm. Compared with typical regularization-based sparse recovery algorithms, BiIST not only can improve the quality of recovered image, but also can obtain a nonsparse solution with retaining phase information and background statistical distribution of the SAR image. Then, targets are classified by the proposed amplitude-phase convolutional neural network (AP-CNN). Typical SAR target classification networks imitate those on optical image, just using amplitude data. However, considering the particularity of the SAR image, the AP-CNN uses both amplitude and phase for training, which theoretically improves the classification accuracy. Experimental results show that the AP-CNN outperforms the typical amplitude-based CNN in target classification, both under standard operating conditions (SOCs) and extended operating conditions (EOCs). Results under SOC demonstrate that the AP-CNN improves the classification accuracy by 11.46% with only 1000 training samples. Even under EOC, the accuracy gap between the Manuscript

In synthetic aperture radar (SAR), due to the characteristics of squint data in spaceborne imaging geometry, a more accurate range cell migration correction (RCMC) operation is necessary. Furthermore, imaging degradation due to ignoring range-variant filtering needed for secondary range compression (SRC) limits SAR imaging. In addition, due to enormous computational complexity required for squint SAR data processing, the existing imaging methods, for instance, chirp scaling algorithm (CSA) and range-Doppler algorithm (RDA) are no longer sufficient, especially for large-scale scenes. In order to solve above problems, this paper presents a novel spaceborne squint SAR sparse imaging method, which could not only eliminate the effects of squint to a certain extent, but also improve the performance of focused SAR image. Compared with matched filtering (MF) based squint imaging algorithm, the proposed method can obtain the SAR images with higher quality from full-or down-sampled echo data.

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Holographic synthetic aperture radar tomography (HoloSAR) combines circular synthetic aperture radar (CSAR) and SAR tomography (TomoSAR) to enable a 360° azimuth observation of the considered scene. This imaging mode achieves a high-resolution three-dimensional (3-D) reconstruction across a full 360°. To capture the deformation information of the observed target, this paper first explores the differential HoloSAR imaging mode, which combines the technologies of CSAR and differential TomoSAR (D-TomoSAR). Then, we propose an imaging method based on the orthogonal matching pursuit (OMP) algorithm and a support generalized likelihood ratio (Sup-GLRT), aiming to achieve high-precision multi-dimensional reconstruction of the surveillance area. In addition, a statistical outlier removal (SOR) point cloud filtering technique is applied to enhance the accuracy of the reconstructed point cloud. Finally, this paper presents the detection of vehicle changes in a parking lot based on the 3-D reconstructed results.