The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B2-2022
30 May 2022
 | 30 May 2022


Y. Yao, B. Zhang, Y. Wan, and Y. Zhang

Keywords: SAR image, Multi-orientation tensor, Multi-orientation tensor index feature, Bilateral matching, Registration

Abstract. The inherent speckle noise in synthetic aperture radar (SAR) images and the significant differences between SAR and optical images in nonlinear radiation give rise to the great difficulty in computing similarity between image features, improving detection accuracy of corresponding points and the efficiency of image matching, thus making the registration of SAR and optical images a long-standing challenging task. To address these issues, a new SAR-optical image registration method was proposed in this paper, namely, Multi-orientation Tensor Index Feature (MoTIF), which is characterized by a lightweight feature descriptor. Specifically, we firstly established a diffusion tensor model based on the information of image gradient orientation. Then, the model was parameterized using polar coordinates to help identify the MoTIF and get the array of indices of maximum value, with which we could draw a multi-orientation index map and thereupon construct the feature vector descriptors. To evaluate the proposed method, seven representative SAR-optical image pairs were tested along with a comparison with other four state-of-the-art methods. Results show that our MoTIF method outperforms the other methods in that it substantially de-speckles SAR images, overcomes nonlinear radiation distortions caused by the differences between SAR and optical images, and achieves high precision and efficiency in image registration. The average number of correct matches (NCM) of 151.0 and the root of mean-squared error (RMSE) of 1.66 pixels obtained by utilizing MoTIF with lower time consumption adds more evidence to its superior performance. The time consumption of the MoTIF method is better than that of the other four methods, and the calculation speed is 4 times faster than that of the LGHD method. Executable code and test data are published in the link