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SAR IMAGE DESPECKLING BY SELECTIVE 3D FILTERING OF MULTIPLE COMPRESSIVE RECONSTRUCTED IMAGES
Author(s) -
Mahboob Iqbal,
Jie Chen,
Wei Yang,
Pengbo Wang,
Bing Sun
Publication year - 2013
Publication title -
electromagnetic waves
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 89
eISSN - 1559-8985
pISSN - 1070-4698
DOI - 10.2528/pier12091504
Subject(s) - artificial intelligence , computer vision , compressed sensing , image (mathematics) , computer science , pattern recognition (psychology)
A despeckling technique based on multiple image reconstruction and selective 3-dimensional flltering is proposed. Multiple SAR images are reconstructed from a single SAR image by employing compressive sensing (CS) theory. In order to obtain multiple images from single SAR image, multiple subsets of pixels are selected from input SAR image by imposing restriction that each subset has at least 20% difierent pixels from any other subset. These subsets are taken as measurement vectors in CS framework to obtain multiple SAR images. A despeckled image is obtained by employing selective 3-dimensional flltering to multiple reconstructed SAR image. The proposed technique is tested on single look complex TerraSAT-X data set, and experimental results exhibit that the proposed technique outperformed benchmark despekling methods in terms of visual quality and despeckling quality metrics.

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