基于深度卷积神经网络的PolSAR图像变化检测方法

针对极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像变化检测问题,提出了结合区域信息和深度卷积神经网络(deep convolutional neural network,DCNN)的PolSAR图像变化检测算法。在本方法中,用超像素分割算法与超像素合并算法提取图像场景的区域信息,利用区域信息和Wishart似然比得到差异图像;再运用预分类算法以得到训练DCNN的伪训练样本和待分类样本;接着用伪训练样本训练DCNN;最后用训练好的DCNN对待分类样本进行分类得到最终结果。实验结果表明,与多种PolSAR变化检测算法相比,所提算法能够获得更好的结果。

Polarimetric SAR image change detection based on deep convolutional neural network

In order to solve the polarimetric synthetic aperture radar (PolSAR) image change detection problem, a PolSAR image change detection method is proposed combining the region information with deep convolutional neural network (DCNN). The superpixel segmentation algorithm and the superpixel combination algorithm are utilized for extracting region information, then a difference image is obtained using region information and Wishart likelihood ratio. Second, a preclassification algorithm is used to obtain the pseudotraining samples and the samples which are ready to be classified. Third, the DCNN is trained using the pseudotraining samples. Finally, the trained DCNN is used to classify the samples that are to be classified to get the final results. Experimental results show that, compared with several existing PolSAR change detection methods, the proposed method can get better results.