Ground armored weapons have a high detection value in military operations. Satellite synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level sizes limited by resolution of sensors. Airborne SAR have strict experimental conditions and cannot be applied in actual battlefield environments. MiniSAR sensors, which combine the advantages of submeter-level ultrahigh resolutions and flexible flight, play a crucial role in recognizing military targets. In this paper, various small military targets in real complex ground scenarios are detected with the MiniSAR of NUAA. However, there are still two difficulties. First, because of a limitation in the number of flight circles, the number of obtainable military target samples is not sufficient to adapt to the traditional deep learning methods that rely on a large number of image samples. Second, due to the imaging systems and different depression angle of MiniSAR, the SAR images of MiniSAR suffer from the same deformation challenge as the moving and stationary target acquisition and recognition (Mstar) with high depression angle. To address these two challenges, we propose a FASAR-Net framework based on few-shot learning with meta learning and adversarial domain learning, combined with the inherent scattering features of the SAR targets. Furthermore, we validate the reliability and accuracy of this algorithm on Mstar and our datasets, and the result of recognizing small SAR targets is compared with our algorithm and other classical algorithms. We conclude that the proposed algorithm has high accuracy in the recognition of the deformation small targets under the few sample condition.

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

There are a large number of lakes with beaded distribution in the semi-arid areas of the Inner Mongolian Plateau, and some of them have degraded or even disappeared during the past three decades. We studied the reasons of the disappearance of these lakes by determining the way of replenishment of these lakes and the impact of the natural-social environment of the basin, with the aim of saving these gradually disappearing lakes. Based on remote sensing image and hydrological analysis, this paper studied the recharge of Daihai Lake and Huangqihai Lake. The deep learning method was used to establish the time-series of lake evolution. The same method was combined with the innovative woodland and farmland extraction method to set up the time-series of ground classification composition in the basins. Using relevant survey data, combined with soil water infiltration test, water chemical, and isotopic signature analysis of various water bodies, we found that the Daihai Lake area is the largest in dry season and the smallest in rainy season and the other lake is not satisfied with this phenomenon. In addition, we calculated the specific recharge and consumption of the study basin. These experiments indicated that the exogenous groundwater is recharged directly through the faults at the bottom of Daihai Lake, while the exogenous groundwater is recharged in Huangqihai Lake through rivers indirectly. Large-scale exploitation of groundwater for agricultural irrigation and industrial production is the main cause of lake degradation. Reducing the extraction of groundwater for agricultural irrigation is an important measure to restore lake ecology.

With the rapid development of deep learning, convolutional neural networks have achieved milestones in synthetic aperture radar (SAR) image object detection. However, object detection in SAR images is still a great challenge due to the difficulty in distinguishing targets from complex backgrounds. At the same time, most of the targets in SAR images are small and unevenly distributed, which makes it challenging to extract sufficient feature information. To solve these issues mentioned above, an efficient object detection network for SAR images based on Swin transformer and YOLOv7 is proposed in this article. First, we design a novel feature aggregation module Petty-specialized feature aggregation (PS-FPN) to enrich small targets’ semantic and spatial features while keeping the model lightweight. PS-FPN module uses the fusion of deep and shallow features by using cross-layer feature aggregation and single-branch feature aggregation to enhance the detection of small targets. Second, a novel attention mechanism strategy mix-attention is proposed to find more attention regions. Finally, we add one more prediction head to extract shallow features that effectively preserve small targets’ feature information. To verify the effectiveness of the proposed algorithm, extensive experiments are carried out on several challenging SAR image datasets. The results show that, compared with other state-of-the-art detectors, the proposed method can achieve significant performance based on lightweight detection.