SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES WITH LOCAL ITERATIVE CLUSTERING AND HETEROGENEOUS STATISTICAL MODEL

D Xiang,W Yan,Y Su,J Wu,H Zhang,W Ni

doi:10.5194/isprs-annals-iv-2-w4-499-2017

Abstract

Abstract. Superpixel segmentation has an advantage that can well preserve the target shape and details. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel segmentation method is proposed. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel segmentation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed segmentation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

Highlights

Object-based segmentation and classification are promising in remote sensing field, which significantly outperform the pixelbased image processing (Niu and Ban 2013; Ban and Jacob 2013)
Numerous superpixel segmentation algorithms have been proposed for optical images, among them, the simple linear iterative clustering (SLIC) (Achanta et al 2012) method is popular and shows good performance in superpixel generation
This paper proposes an adaptive superpixel segmemtaton method for PolSAR images

Summary

Introduction

Object-based segmentation and classification are promising in remote sensing field, which significantly outperform the pixelbased image processing (Niu and Ban 2013; Ban and Jacob 2013). Superpixel generation and segmentation for PolSAR images, especially in heterogeneous urban areas, still remains unsolved. Another drawback of these methods lies on the nonadaptive selection of trade-off factor, which balances the polarimetric similarity and spatial proximity while simultaneously provides control over the shape and compactness of superpixels. This parameter is usually set manually to a constant value by trial and error, which might not be suitable in some areas

Methods

Results

Conclusion