Adaptive scale segmentation algorithm for polarimetric SAR image. Issue 19 (5th July 2019)

• Record Type: Journal Article
• Authors: Xu, Yifan; Liu, Aifang; Huang, Long
• Is Part Of: Journal of engineering. Volume 2019:Issue 19(2019)
• Page Start: 6072
• Page End: 6076
• Publication Date: 2019-07-05
• Subjects: radar polarimetry -- image segmentation -- image classification -- radar imaging -- synthetic aperture radar -- remote sensing by radar -- geophysical image processing; global heterogeneity evaluation index -- scattering matrix -- polarimetric SAR data -- polarimetric SAR image -- fixed scale segmentation algorithms -- small‐scale objects -- large‐scale objects -- segmentation boundaries -- segmentation scales -- PolSAR classification -- synthetic gradient image -- adaptive scale segmentation algorithm -- PolSAR image; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0408
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : Polarimetric SAR (PolSAR) data can be characterised by scattering matrix, which contains four elements. It is difficult to merge all the elements of the scattering matrix for segmentation. On the other hand, a PolSAR image contains objects of various scales, so segmentation on a single scale may lead to over‐segmentation and under‐segmentation. To address these two problems, an adaptive scale segmentation algorithm for PolSAR image is proposed. First, extract a synthetic gradient image that contains scattering information of all polarisation channels. Second, to prevent the over‐segmentation of the watershed algorithm, extract the markers of the synthetic gradient image adaptively. Finally, combined with the result of PolSAR classification, the segmentation scales are adaptively selected for the segmentation of the synthetic gradient image. The results show that the proposed algorithm can not only accurately extract the segmentation boundaries of different objects but also overcome the over‐segmentation of large‐scale objects and under‐segmentation of small‐scale objects. Compared to fixed scale segmentation algorithms, the segmentation result of the proposed algorithm has the best quantitative index, that is, the lowest global heterogeneity evaluation index.