Polarimetric synthetic aperture radar (PolSAR) image classification is a challenging task due to lack of effective feature representation approaches. However, when it comes to deep feature representation, there is little research that pays enough attention to make full use of existing expert knowledge. We propose a model for deep polarimetric feature extraction, and a superpixel map is used to integrate contextual information. The proposed model uses multiple polarimetric algebra operations, polarimetric target decomposition methods, and convolutional neural network (CNN) to extract deep polarimetric features. The core idea is to utilize expert knowledge of the target scattering mechanism interpretation to assist the CNN classifier in feature extraction and employ superpixel algorithm based on Wishart distribution to improve the final classification performance. The proposed method is able to get abstract and discriminative representation from initial features, achieving robust and improved performance for PolSAR images. Compared with other state-of-the-art methods, experiments on the classification of three real PolSAR datasets are performed to demonstrate the superiority of the proposed approach.
Superpixel-level image classification methods take advantage of contextual information of pixels and reduce the time cost in training and test processes. However, extracting a superpixel-level feature is a challenging task because each superpixel has irregular size and shape. A superpixel-level polarimetric feature extraction (SLPFE) based on circular loop spatial pyramid pooling (CLSPP) (SLPFE_CLSPP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. The main idea is that the CLSPP layer divides the feature maps of each superpixel into the same number of circular loops and produces consistent feature size for each superpixel, which then feeds into the next processing step, such as classification. SLPFE_CLSPP not only makes it possible to generate the same size feature but also to utilize the spatial information of pixels and reduce the running time during SLPFE process. Compared with the existing methods, the proposed method makes a balance between overall accuracy and running time. Experimental results on real PolSAR dataset demonstrated the superiority of the proposed approach.
In response to the increasing threat of terrorism, standoff security screening of person-borne concealed weapons or contraband in public areas, such as airports and railway stations, is an urgent requirement; however, current solutions suffer from the limited field of view and the problem of security screening with interfering with the passenger flow. This paper proposes an imaging method that exploits the angular diversity of distributed multistatic terahertz (THz) radar network in combination with inverse synthetic aperture radar (ISAR) technique to realize high-resolution penetration imaging at a standoff range. The radar network is based on multistatic ISAR systems, and the multistatic ISAR system’s equivalent monostatic model is derived. According to the equivalent monostatic model, we develop the image formation algorithm and give the analytical expression of the cross-range resolution. Compared with conventional monostatic ISAR configuration, the analytical expression shows that the multistatic ISAR configuration can enhance the image’s cross-range resolution and the maximum enhanced factor is approximately equal to the number of radars in the multistatic system. Numerical simulations and experimental results agree well with the theoretical derivation. The proposed imaging approach offers a low-cost and flexible alternative to the present systems not only for security screening but also for noncooperative target imaging.
The high-resolution inverse synthetic aperture radar (ISAR) imaging of the maneuvering target with small-angle measurements is expected, which can be achieved by using the radar operating at a higher central frequency. However, because the defocus induced by the spatially variant phase error becomes significant with the resolution improvement and the central frequency increasing, the global phase correction is invalid. Moreover, due to the scattering characteristics of aspect-dependent scatterers and small-angle measurements, we can rarely obtain the rough outline of the maneuvering target. To deal with these difficulties, we propose an ISAR imaging method for the maneuvering target with distributed high resolution radars. To mitigate the spatially variant phase error, we correct phase errors in patches based on the weighted least-squares algorithm, which makes the image well-focused. Then, the ISAR imaging method with distributed radars is presented to obtain the fusion image, with the object to capture enough scatterers to represent the maneuvering target. The fusion image could provide more details. Real data have verified the effectiveness of the proposed method.
In recent years, spaceborne/airborne hybrid bi-static synthetic aperture radar (SA-BSAR) has been proposed as a new
kind of microwave remote sensor. The SA-BSAR system combines the invulnerability of the spaceborne SAR with high
resolution and flexibility of the airborne SAR. To decrease the cost for developing a new system, a suitable flexible and
powerful simulation tool is essential. Nevertheless, the currently available SAR simulators, which were developed for
other SAR constellation, are not sufficient for SA-BSAR. This paper presents a new simulation architecture. To improve
the flexibility and the suitability of the architecture, both the requirement of SA-BSAR and the requirements of other
kinds of bi- and multi-static SAR are all fulfilled in designing the architecture. Modular configuration is the key for the
flexibility. The requirements, the framework and the steps in performing the simulator are discussed in detail.
Both real-time rate and resolution both are key indexes of Synthetic Aperture Radar(SAR)imaging, but there is a
conflict between them. Real-time imaging becomes difficult because of the large computational requirement posed by
high-resolution processing. Parallel computing is an effective approach for real-time processing. In previous research,
coarse and medium grained parallel algorithms for SAR imaging have been presented. Although they can significantly
improve the processing speed, the quality of image has been ignored. Subaperture is widely used in high-resolution SAR.
Compared with full aperture processing, it can compensate the motion errors more accurately and get better images.
Whereas, subaperture processing can't be applied in existing parallel imaging algorithms because of they are all based
on full aperture processing, which restricts the application of existing algorithms in high-resolution SAR parallel
imaging. This paper presents a parallel imaging algorithm for
high-resolution SAR, through which we can obtain
high-resolution SAR image while achieving good computation efficiency. It combines chirp-scaling algorithm with
subaperture processing. The new algorithm can highly effectively run on parallel computer, in which each node has the
same load. It reduces the large communication requirement posed by three transposes through designing CS processing
for subaperture data, and it has better parallel scalability, which means that it can be used on larger parallel computer
without deducing the image quality. The experiments on SGI Origin2000 have proved that, compared with medium
grained parallel CS algorithm, the algorithm presented in this paper is more suitable for high-resolution SAR parallel
Inverse synthetic aperture radar (ISAR) is a powerful means in target identifying, especially the target in the air, which can image the moving target. There is little study on modeling and resistance technique according to ISAR in China. This paper establishes a model of ISAR system, and then studies on some valid jamming technique. This will provide us the valid technique support on ISAR resistance equipment later.
With the development of SAR processing techniques, high image precision and high real time rate have become an important index, especially on the military field. This paper presents a medium grained parallel processing algorithm where every processing stage is done in parallel, and the degree of parallelism is task-level. It is fit for the parallel computer with good communication capacity. The experiments on DAWNING3000 shows this parallel processing algorithm can get good results on real time rate and processing efficiency.