A Superpixel-Based Neighborhood Polarimetric Covariance Matrix for Polsar Ship Detection

In a recent work, a neighborhood polarimetric covariance matrix [N] was proposed to detect ships from polarimetric SAR (PolSAR) imagery. However, its computational complexity is extremely high. Besides, the heterogeneity surrounding ship edges is also not well considered in [N]. To cure these draw-backs, we construct a novel superpixels-based neighborhood polarimetric covariance matrix [SN] in this paper. Specifically, the simple linear iterative clustering (SLIC) is first used to obtain superpixels. Then, the vector v mean corresponding to the mean value of superpixel is further computed so as to characterize the neighborhood information of each pixel in superpixel. Finally, by combining the original scattering vector v and v mean together, the vector t 12 is built to calculate [SN]. The experiment tested on one L-Band ALOS PolSAR imagery shows that i) the polarimetric whitening filter derived from [SN] (i.e., PWF SN ) has a better detection performance than that derived from [N] (i.e., PWF N ); ii) the calculation process of [SN] takes much less time than that of [N].