Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images.

Due to the superior flexibility and cost-efficiency, unmanned aerial vehicle (UAV) is becoming an indispensable platform for advanced remote sensing applications. In this paper, the performance and implementation of the energy-efficient passive UAV radar imaging system is investigated. Equipped with a synthetic aperture radar (SAR) receiver, the UAV platform passively reuses the backscattered signal of an external illuminator, and achieves SAR imaging and data communication. Firstly, we present the system concept and block diagram. Then, the imaging performance and feasibility are analyzed for the typical illuminators. It is found that the system can achieve distinct mission performance with different kinds of illuminators, which offers more alternatives for various mission requirements. A set of mission performance evaluators is established to comprehensively assess the capability of the system, including UAV navigation, passive SAR imaging and communication. An energy-efficient path planning method is then presented to generate a UAV path with optimized mission performance. Numerical simulations are conducted to verify the effectiveness of the performance evaluators and path planning method. Finally, a real implementation of the system is given. The imaging results of an airborne passive SAR experiment using external illuminator further validates the imaging capability and performance analysis of the system.

Due to the dispersion nature of the ionosphere, the spaceborne polarimetric synthetic aperture radar (PolSAR) systems at L or lower frequencies will generally experience more severe effects in comparison to frequencies above L-band. Correspondingly, according to the mechanism of how ionosphere destroys the scattering matrix of PolSAR data, the total electron content (TEC), an important input in computerized ionospheric tomography (CIT), can be retrieved by model inversion. Thus, this paper presents a technique of CIT based on the spaceborne PolSAR data. The CIT technique is a means to obtain the spatial distribution of ionospheric electron density from the TEC values. Because of the high spatial resolution of spaceborne SAR, the kilometer-scale TEC information can be obtained by the PolSAR data where such resolution was previously inaccessible. The spatial distribution of ionospheric electron density with high resolution can therefore be obtained. In addition, the potential limitation of previous CIT based on SAR imaging information is the difficulty in requiring strong point targets with high signal-to-clutter (SCR) ratio in scenes. In contrast, the CIT reconstruction based on scattering matrix information can be easily realized without aforementioned special requirements. In this paper, by using the simulated data of the Advanced Land Observing Satellite (ALOS) Phased-Array L-band Synthetic Aperture Radar (PALSAR) full-pol data, the International Reference Ionosphere (IRI) 2007, and the International Geomagnetic Reference Field (IGRF) models, the reconstruction of ionospheric electron density distribution is first simulated to validate the feasibility of the proposed technique. Then, possible effects of SAR system and model errors on the reconstruction results are also simulated and analyzed to show the applicability.