PolSAR images provide rich polarimetric information, however, due to the limitations of the imaging system, the spatial resolution decreases while the richer polarimetric information is obtained. The lower resolution limits the application, so it is necessary to use super-resolution technology to improve the spatial resolution. In this paper, in response to the low spatial resolution of PolSAR images, a PolSAR super-resolution framework is proposed to improve the spatial resolution by the use of a residual convolutional neural network. Within this framework, deconvolution is used to up-sample the PolSAR images, PReLU is added to maintain the numerical properties. A complex structure block is also designed to accommodate the PolSAR data structure. In addition, prior information on the low-resolution image itself is used to reduce the artifacts. The proposed method shows a superior performance when compared to the traditional methods in both the quantitative evaluation and visual assessment. The proposed method improved the spatial resolution significantly, especially in terms of detail information retention, and it improves the mean PSNR by more than 12% when compared to the traditional methods. By analyzing the phase statistics and polarimetric response, it is shown that the proposed method has a good polarimetric information retention ability, and can obtain a higher classification accuracy.

PolSAR图像提供了丰富的极化信息，但是，由于成像系统的局限性，空间分辨率降低了，同时获得了更丰富的极化信息。较低的分辨率限制了应用程序，因此有必要使用超分辨率技术来提高空间分辨率。针对PolSAR图像空间分辨率低的问题，提出了一个PolSAR超分辨率框架，以利用残差卷积神经网络提高空间分辨率。在此框架内，使用反卷积对PolSAR图像进行上采样，并添加PReLU以保持数值特性。还设计了一个复杂的结构块来容纳PolSAR数据结构。另外，关于低分辨率图像本身的先验信息用于减少伪像。与传统方法相比，该方法在定量评估和视觉评估方面均表现出了优异的性能。与传统方法相比，该方法显着提高了空间分辨率，特别是在细节信息保留方面，并且将平均PSNR提高了12％以上。通过对相位统计和极化响应的分析，表明该方法具有良好的极化信息保持能力，可以得到较高的分类精度。与传统方法相比，它可以将平均PSNR提高12％以上。通过对相位统计和极化响应的分析，表明该方法具有良好的极化信息保持能力，可以得到较高的分类精度。与传统方法相比，它可以将平均PSNR提高12％以上。通过对相位统计和极化响应的分析，表明该方法具有良好的极化信息保持能力，可以得到较高的分类精度。