Synthetic aperture radar (SAR) target detectors based on deep learning have difficulty finding a good balance between accuracy and speed. Current pruning methods are usually used for backbone consistent pruning and seldom directly for the whole structure of deep learning target detectors; therefore, for edge-end applications, this article proposes a new full-link general automatic pruning algorithm for SAR target detectors, referred to as SARGap. First, SARGap automatically analyzes the network structure by creating a dependency graph, divides the pair-coupled network structure into the same group, and prunes the same channel for the same group of network structures so that the algorithm can be applied to a variety of complex target detectors. Second, an automatic pruning rate search method (APRS) is designed to search for the optimal pruning rate of each group of network structures in the target detector. Finally, to find a good balance between precision and speed in the automatic search of the pruning rate, a multiobjective optimization loss function (MOOL) is constructed as the APRS objective function. A series of experiments based on SSDD and HRSID, two large-scale SAR target detection datasets, are carried out to prove the superiority of this method. Using Yolov5s as the baseline, SARGap can compress parameters by 84.29%/82.86% and flops by 80.50%/81.93% on two datasets with almost no loss of accuracy. In addition, SARGap can be applied to any deep learning target detector and match hardware computing resources to achieve optimal full-link pruning.

In order to more efficiently mine the features of polarimetric synthetic aperture radar (PolSAR) and establish a more appropriate classification model, this article proposes an improved convolutional autoencoder (ICAE) based on texture feature fusion (TFF-ICAE) for PolSAR terrain classification. First, TFF-ICAE specifically designs a multi-indicator squeeze-and-excitation (MI-SE) block and incorporates it into the CAE network. MI-SE can enhance the essential feature information while suppressing the interference information as much as possible, and it can effectively increase the between-class distance while reducing the within-class distance. Then, TFF-ICAE uses gray level co-occurrence matrix (GLCM) to capture the texture features, and it optimally fuses these texture features and the deep features extracted by ICAE to complete the multilevel feature fusion, elevating the feature representation completeness of the terrain. That is, TFF-ICAE effectively enhances the feature separation capability of different categories while greatly elevating the feature representation completeness. Experiments on the datasets of San Francisco, Oberpfaffenhofen, and Flevoland show that the proposed TFF-ICAE, respectively, achieves overall accuracies of 93.44%, 97.61%, and 97.78%, which are at least 0.92%, 1.52%, and 0.97% higher than other algorithms. Undoubtedly, the superiority of TFF-ICAE is verified on these datasets.

Unlike optical images, synthetic aperture radar (SAR) images have unique characteristics, such as few samples, strong scattering, sparseness, multiple scales, complex interference and background, and inconspicuous target edge contour information. Current SAR target detection algorithms have difficulty in balancing accuracy and speed, and the performance of these algorithms is relatively limited, thus making it difficult to deploy practical applications. To this end, this article proposes AFSar, an innovative anchor-free SAR target detection algorithm based on multiscale enhancement representation learning. First, we introduce the latest anchor-free architecture YOLOX as the basic framework. Second, to reduce the computational complexity of the model and to improve the ability of multiscale feature extraction, we redesigned the lightweight backbone, namely, MobileNetV2S. Furthermore, we propose an attention enhancement PAN module, called CSEMPAN, which highlights the unique strong scattering characteristics of SAR targets by integrating channel and spatial attention mechanisms. Finally, in view of the multiscale and strong sparse characteristics of SAR targets, we propose a new target detection head, namely, ESPHead. ESPHead extracts the features of targets with different scales by using dilated convolution with different dilated rates, so as to enhance the detection ability of the model for targets with different scales. The results of ablation experiments on the SSDD dataset show that the mAP of our algorithm reaches 0.977, while the Flops is only 9.86 G, achieving state of the art.

At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of SAR images have rarely been explored. To this end, this paper proposes a lightweight meta-learning-based SAR image few-shot object detection method, which improves the accuracy and speed of SAR image few-shot object detection from a more balanced perspective. First, we introduce the latest FSODM method in optical remote sensing as a benchmark framework. Second, a lightweight meta-feature extractor named DarknetS is designed to enhance the feature representation of SAR images and improve detection timeliness. Furthermore, we build a new aggregation module called AggregationS, which encodes support features and query features into the same feature subspace via a novel transformer encoder. This module design can better extract the correlation and saliency between different classes in the support set, improve the detection accuracy of the query set, and enhance the detection generalization performance of new classes. Finally, we built several real-world SAR image few-shot object detection datasets to verify the effectiveness of the method. Experimental results show that FSODS can achieve a better object detection performance compared to the baseline model under the condition that only a small amount of labelled data is required for new classes of SAR image objects.

An L-band geosynchronous synthetic aperture radar (GEO SAR) system has to be confronted by an intractable issue of the decorrelations imposed by ionospheric irregularities. On the one hand, the phase and amplitude scintillations will bring about the decorrelation within the synthetic aperture and result in azimuth-imaging degradation. On the other hand, the imposed scintillation history is spatially decorrelated across the ultra-large GEO SAR scene. In this article, a signal model of the GEO SAR acquisitionis established with the two-way ionospheric transfer function (ITF) modulation to incorporate these two types of decorrelations. This model meanwhile takes the anisotropic and flowing irregularities into account. By using this model, the L-band GEO SAR azimuth-imaging is evaluated in terms of five indexes, whose performances are dependent on nine ionospheric parameters. Furthermore, the spatial correlation of the phase and intensity scintillation histories is investigated for the L-band GEO SAR scene, both in simulation and statistics. The statistical result implies a sized scene, in which the phase scintillation history tends to be consistent. Finally, the interferometric performance is investigated between the pure and contaminated GEO SAR images. The simulation result shows that the degradation of the interferometric coherence results from the in-aperture decorrelation.

There has been a burgeoning prospect in developing a spaceborne P-band synthetic aperture radar (SAR) mission for its stronger penetrability through foliage and subsurface than the higher-frequency system. However, the transionospheric signals operating at P-band are more susceptible to scintillation impacts, which may bring about the decorrelation of the signal amplitude, phase, and frequency introduced by ionospheric irregularities. In this article, a comprehensive numerical model of the generalized ambiguity function (GAF) is established to evaluate SAR imaging deterioration. On the one hand, an improved two-frequency and two-position coherence function (TFTPCF) is integrated in the GAF to include the amplitude scintillation derived from the diffraction. On the other hand, the anisotropic characteristics of the irregular structure is introduced into TFTPCF by adopting the Rino's 2-D spectrum. Furthermore, the ambiguous resolution is redefined for a more strict numeration. Numerical analyses about the ionospheric coherence and an ambiguous resolution are performed to investigate their sensitivity to scintillation parameters. Results show that this model is capable of depicting more comprehensive effects of the anisotropic irregular ionosphere, which may exhibit a rod-like structure, including elongation by anisotropic scale, rotation by geomagnetic heading, and projection by geomagnetic inclination. At last, the signal-level simulations are operated to verify the effectiveness of numerical conclusions, which further confirm that the structural configuration of anisotropic irregularities has a complicated effect on spaceborne P-band SAR image resolution.

In this paper, the anisotropic generality of the ionospheric irregularities is incorporated in the generalized ambiguity function (GAF) to evaluate its impact on spaceborne P-band synthetic aperture radar (SAR) imaging. The configuration of the anisotropic ionosphere exhibits as a rod-like structure, which is elongated by anisotropic scale, rotated by magnetic heading and projected by geomagnetic inclination. Aiming at these three parameters, numerical analysis is implemented in terms of the coherence and ambiguous resolution. At last, signal-level simulation is carried out to validate the effectiveness of the numerical results.