Paper Info
Title
Combined algorithm for improvement of fused radar and optical data classification accuracy.
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. (C) 2017 SPIE and IS&T
Year
DOI
Venue
2017
10.1117/1.JEI.26.1.013017
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
Linear discriminant analysis,sparse regularization,radar and optical data fusion,Sentinel,rotation forest
Feature selection,Computer science,Synthetic aperture radar,Artificial intelligence,Discriminative model,Radar,Computer vision,Pattern recognition,Feature (computer vision),Matrix decomposition,Support vector machine,Algorithm,Linear discriminant analysis
Journal
Volume
Issue
ISSN
26
1
1017-9909
Citations 
PageRank 
References 
1
0.35
21
Authors
4
Name
Order
Citations
PageRank
Danya Karimi111.02
Kazem Rangzan241.14
Gholamreza Akbarizadeh3516.19
Mostafa Kabolizade410.35