Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields

In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method.

Automated summary

The study proposes a classification model based on feature-level fusion of optical images and Synthetic Aperture Radar (SAR), improving feature identification capabilities. By introducing feature importance to enhance classification accuracy, the dataset combining the two shows significant improvements over using optical or polarized SAR image features alone. The random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper achieved the best overall accuracy and Kappa coefficient among the four classification models used, validating the effectiveness of the method.