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Combined algorithm for improvement of fused radar and optical data classification accuracy

[+] Author Affiliations
Danya Karimi, Kazem Rangzan, Mostafa Kabolizadeh

Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Gholamreza Akbarizadeh

Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

J. Electron. Imaging. 26(1), 013017 (Feb 22, 2017). doi:10.1117/1.JEI.26.1.013017
History: Received September 10, 2016; Accepted January 27, 2017
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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.

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Citation

Danya Karimi ; Kazem Rangzan ; Gholamreza Akbarizadeh and Mostafa Kabolizadeh
"Combined algorithm for improvement of fused radar and optical data classification accuracy", J. Electron. Imaging. 26(1), 013017 (Feb 22, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013017


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