Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning

Caiqiong Wang; Lei Zhao; Wangfei Zhang; Xiyun Mu; Shitao Li

doi:10.7717/peerj.12805

Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning

PeerJ. 2022 Jan 19:10:e12805. doi: 10.7717/peerj.12805. eCollection 2022.

Authors

Caiqiong Wang^{1

2

3}, Lei Zhao^{2

3}, Wangfei Zhang¹, Xiyun Mu⁴, Shitao Li⁵

Affiliations

¹ College of Forestry, Southwest Forestry University, Kunming, Yunnan, China.
² Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China.
³ Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing, China.
⁴ Institute of Chifeng Forestry Research, Chifeng, Inner Mongolia, China.
⁵ College of Geography and Ecotourism, Southwest Forestry University, Kunming, Yunnan, China.

Abstract

Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can't be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image.

Keywords: Mean-shift; Multi-temporal; Normalized cut; Polarimetric SAR; Spectral graph partitioning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Spectrum Analysis

Grants and funding

This research was funded by the National Natural Science Foundation of China (41801289), the National Science and Technology Major Project of China’s High Resolution Earth Observation System (21-Y20B01-9001-19/22) and the National Natural Science Foundation of China (42161059; 31860240; 32160365). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.