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Adaptive superpixel generation for time-series PolSAR images considering time-varying characteristics

  • role： First author第一作者
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

  • Email：yejiawei@csu.edu.cn
  • Introduction：叶家伟，研究方向为时序极化SAR图像处理。E-mail： yejiawei@csu.edu.cn
YE Jiawei,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

  • Email：wangchangcheng@csu.edu.cn
  • Introduction：汪长城，研究方向为遥感图像处理、SAR/极化SAR图像处理。E-mail： wangchangcheng@csu.edu.cn
WANG Changcheng*,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

GAO Han,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

SHEN Peng,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

SONG Tianyi,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

HU Chihao

résumé

Superpixel generation is an important pre-processing step in the object-level data processing system, which is of great practical significance for the efficient processing and application of multi-temporal and multi-polarized SAR data. The single-temporal superpixel segmentation method does not fully utilize the complete scattering information of the segmented objects in the time series. To address this problem, this paper proposes a multi-temporal PolSAR Images adaptive cooperative segmentation method based on the Simple Linear Iterative Clustering (SLIC) model, which takes full use of the advantages of fully observed and describable time-varying characteristics of the time-series PolSAR data.Firstly, this method calculates the time-series PolSAR similarity distance based on Wishart distribution by uniting the polarization covariance matrix of multi-temporal; then uses multi-temporal polarization SAR data to perform gradient calculation to detect image edges; Finally, a homogeneity measure factor based on multi-temporal polarimetric SAR edge detection is proposed to adaptively balance the weight relationship between polarimetric distance and spatial distance.In this paper, we used 8 Radarsat-2 quad-polarization SAR images to evaluate the effectiveness of this method in terms of both visualization effect and quantitative accuracy. The results show that the method in this paper outperforms the single-temporal PolSAR superpixel generation method and the existing traditional multi-temporal PolSAR superpixel method. For example, as for the superpixel generation result with the quad-polarization SAR data (K = 12000), the value of the boundary recall(BR) and the achievable segmentation accuracy(ASA) by the proposed similarity measure and homogeneity factor is about 93.58% and 95.13%, respectively.To address the problem that the single-temporal polarization SAR segmentation does not consider the time-varying characteristics of the ground object polarization characteristics, this paper proposes a multi-temporal polarization SAR image adaptive collaborative segmentation method based on the SLIC model. The experimental results show that compared with the segmentation method based on single-temporal data and the traditional multi-temporal polarimetric SAR superpixel segmentation method, the superpixels generated by this paper have obvious advantages in both visualization effect and quantitative accuracy, and can effectively fit the ground truth boundary, which proves that the proposed method is an effective superpixel generation method.

mots-clés

remote sensing;PolSAR;image segmentation;SLIC;superpixels;multi-temporal

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