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Journal of Electronic Imaging

Combined algorithm for improvement of fused radar and optical data classification accuracy
Author(s): Danya Karimi; Kazem Rangzan; Gholamreza Akbarizadeh; Mostafa Kabolizadeh
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Paper Abstract

A new method, MICO-LDASR, is proposed to improve the classification accuracy of fused radar and optical data. The proposed algorithm combines three algorithms: multiplicative intrinsic component optimization (MICO), linear discriminant analysis (LDA), and sparse regularization (SR). MICO-LDASR first corrects the bias fields of the input images by an energy minimization process and then selects the most discriminative image features using a combination of LDA and SR (LDASR) based on a supervised feature selection and learning. Two pairs of fused radar and optical data were used in this study. Features, such as non-negative matrix factorization and textural features, were extracted from the original and bias corrected images, and, following the formation of two different types of feature matrices, the matrices were optimized based on LDASR and utilized in the two learned and unlearned forms as the inputs to rotation forest and support vector machine classifiers. The results showed that classification accuracy is greatly improved when implementing MICO-LDASR on feature matrices of Sentinel and ALOS-fused data.

Paper Details

Date Published: 22 February 2017
PDF: 10 pages
J. Electron. Imag. 26(1) 013017 doi: 10.1117/1.JEI.26.1.013017
Published in: Journal of Electronic Imaging Volume 26, Issue 1
Show Author Affiliations
Danya Karimi, Shahid Chamran Univ. of Ahvaz (Iran, Islamic Republic of)
Kazem Rangzan, Shahid Chamran Univ. of Ahvaz (Iran, Islamic Republic of)
Gholamreza Akbarizadeh, Shahid Chamran Univ. of Ahwaz (Iran, Islamic Republic of)
Mostafa Kabolizadeh, Shahid Chamran Univ. of Ahvaz (Iran, Islamic Republic of)

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