Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of SAR images into different bands, an SAR image fusion classification method based on the decision-level combination of multi-band information is proposed in this paper. Within the proposed method, the idea of Dempster–Shafer evidence theory is introduced to model the uncertainty of the classification result of each pixel and used to combine the classification results of multiple band SAR images. The convolutional neural network is used to classify single-band SAR images. Calculate the belief entropy of each pixel to measure the uncertainty of single-band classification, and generate the basic probability assignment function. The idea of the term frequency-inverse document frequency in natural language processing is combined with the conflict coefficient to obtain the weight of different bands. Meanwhile, the neighborhood classification of each pixel in different band sensors is considered to obtain the total weight of each band sensor, generate weighted average BPA, and obtain the final ground object classification result after fusion. The validity of the proposed method is verified in two groups of multi-band SAR image classification experiments, and the proposed method has effectively improved the accuracy compared to the modified average approach.

Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms.