Semantic Segmentation of Polarimetric SAR Image Based on Dual-Channel Multi-Size Fully Connected Convolutional Conditional Random Field

The traditional fully connected convolutional conditional random field has a proven robust performance in post-processing semantic segmentation of SAR images. However, the current challenge is how to improve the richness of image features, thereby improving the accuracy of image segmentation. This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. Firstly, the full-polarization SAR image and the corresponding optical image are input into the model at the same time, which can increase the richness of feature information. Secondly, multi-size input integrates image information of different sizes and models images of various sizes. Finally, the importance of features is introduced to determine the weights of polarized SAR images and optical images, and CRF is improved into a potential function so that the model can adaptively adjust the degree of influence of different image features on the segmentation effect. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method.

Automated summary

This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. By inputting the full-polarization SAR image and the corresponding optical image simultaneously, integrating multi-size inputs, and introducing the importance of features, the accuracy of image segmentation is improved.