Fractional Order Spectrum of Cumulant in SAR Image Registration
Jianjun Sun, Yan Fei Zhao, Xinbo Li, Shigang Wang, Jian Jun Wei, Zhenhao Wang · IEEE Transactions on Geoscience and Remote Sensing · 2024
Fractional order cumulant (FOC) is a new tool that can suppress symmetric$\alpha $stable (S$\alpha $S) noises in signal processing. Although FOC has obvious advantages for its noise suppression ability, some weaknesses still limit its application in synthetic aperture radar (SAR) image processing. The main challenge is that FOC can only suppress S$\alpha $S noises when all angular frequencies are zeros. In order to provide a statistic with noise robustness for SAR image processing, we propose a new concept of fractional order spectrum of cumulant (FOSC). FOSC can cancel the impact of S$\alpha $S noises without the limitation of angular frequencies in theory, which means FOSC can represent the local features more accurately. Furthermore, a FOSC-based SAR image registration is proposed to verify the advantage of FOSC. First, the 2-D formats of FOSC with different angular frequencies are calculated to provide more image features. Second, a multi-frequency pyramid array is designed to utilize the additional information in FOSC images, which can be used to detect more accurate keypoints. Third, the local descriptors based on FOSC are designed, which are constructed by accumulating orientation histograms of gradients based on FOSC and re-arranging elements in feature vectors regularly based on the orientation assignment. Finally, rotation consistencies are designed and used to eliminate the mismatching points after Euclidean distance-based matching of feature vectors. The proposed method is compared with four state-of-the-art methods. Experimental results show that the proposed method has achieved an impressive SAR image registration with 16.47%–48.37% gains when evaluating using the similarity of stitched areas.