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access icon openaccess Adaptive scale segmentation algorithm for polarimetric SAR image

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 20/02/2019, Accepted 03/05/2019, Published 30/05/2019

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.

Inspec keywords: radar polarimetry; synthetic aperture radar; geophysical image processing; image classification; radar imaging; remote sensing by radar; image segmentation

Other keywords: scattering matrix; large-scale objects; global heterogeneity evaluation index; segmentation scales; adaptive scale segmentation algorithm; synthetic gradient image; PolSAR image; small-scale objects; PolSAR classification; polarimetric SAR image; polarimetric SAR data; fixed scale segmentation algorithms; segmentation boundaries

Subjects: Computer vision and image processing techniques; Geophysics computing; Image recognition; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Geophysical techniques and equipment; Data and information; acquisition, processing, storage and dissemination in geophysics; Radar equipment, systems and applications

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