For synthetic aperture radar (SAR), multi-polarization and multi-frequency modes greatly enrich the acquired earth resource information and have been widely applied in remote sensing fields. In this paper, we compare the classification capabilities of multi-polarization and dual-frequency SAR. To meet the objective of selecting consistent and complete polarimetric information, a unified classification framework is proposed. In the framework, covariance matrices are used directly as inputs instead of polarimetric indicators. Additionally, the Wishart mixture model (WMM) is utilized to characterize the statistical distribution of polarimetric SAR data. Besides, the data log-likelihood function is utilized to mitigate the influence of the initial values on the expectation-maximization (EM) algorithm. Then, among the combinations of four sample-to-subclass distances and two schemes for obtaining sample-to-class distances, the one with the best classification performance is selected as the default for this framework. In the experiments, the classification capabilities of full polarization (FP), compact polarization (CP), and dual polarization (DP) modes are first compared through the proposed classification framework. Then, we compare the classification capabilities of dual-frequency SAR in FP, CP, and DP modes. For PolSAR system design, it is necessary to strike a balance between demand indexes (classification performance, coverage width, and so on) and cost (such as budget, weight, and so on). The comparison results provide a reference for the optimization of polarization modes and frequency bands of the existing multi-polarization and dual-frequency SAR payloads and the design of future SAR systems.

Polarimetric synthetic aperture radar (SAR) provides an effective way to understand the physical properties of planetary surfaces. Rocks are the products of specific geological events, recording the evolution of impact craters on airless bodies. Multiple-scattered radar echoes from the surface or near-surface rocks generally exhibit high circular polarization ratio (CPR). In this paper, we systematically investigate the relationship between radar CPR and rock abundance of lunar regolith using Mini-RF SAR data. A total of 51 craters with diameters of 11.8-200 km are selected for analysis. The mean values of Mini-RF CPR and Diviner rock abundance are calculated for the interior and exterior of each crater. Statistical results show a close correlation between Mini-RF CPR and Diviner rock abundance, where high radar CPR and Diviner rock abundance are observed at the blocky ejecta. However, we find the difference of mean CPR value between crater interior and exterior is smaller than that of Diviner rock abundance. This may be attributed to the near surface rocks that contribute to radar CPR but are not considered in the inversion of Diviner rock abundance. Therefore, the concept of volumetric rock abundance is introduced, indicating the proportion of rocks with diameters larger than one-tenth of the radar wavelength within the detection area. A three component CPR model is then developed to link radar CPR to volumetric rock abundance. Furthermore, an inverse powerlaw relationship between volumetric rock abundance and crater ages is discovered, which further indicates the feasibility of using SAR data to estimate rock abundance of lunar regolith.

Owing to the Moon’s rough surface, there is a growing controversy over the conclusion that water ice exists in the lunar permanently shadowed regions (PSRs) with a high circular polarization ratio (CPR). To further detect water ice on the Moon, an innovative design method for spaceborne synthetic aperture radar (SAR) system is proposed, to obtain radar data that can be used to distinguish water ice from lunar regolith with a small difference in the dielectric constants. According to Campbell’s dielectric constant model and the requirement that SAR radiometric resolution is smaller than the contrast of targets in images, a newly defined SAR system function involved in the method is presented to evaluate the influence of some system parameters on the water ice detection capability of SAR. In addition, several simulation experiments are performed, and the results demonstrate that the presented SAR design method may be helpful for lunar water ice exploration.

The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance.