TSPol-ASLIC: Adaptive Superpixel Generation with Local Iterative Clustering for Time-series Quad- and Dual-Polarization SAR Data

The superpixel generation is a key step for object-based classification and change detection. For the time-series PolSAR superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this paper, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named as TSPol-ASLIC. There are three main improvements. Firstly, a novel time-series polarimetric similarity measure based on the root mean square (RMS) is proposed. Multi-temporal polarimetric statistical information are combined to describe the polarimetric proximity between pixels, referring to the RMS of the multi-temporal proximities. Secondly, an edge detection method based on the stacked two-dimensional Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Thirdly, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use 8 Radarsat-2 quad-polarization SAR images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively