Gaussian kernel-based Fuzzy Rough Set for information fusion of imperfect images

Abstract

Imperfection of remote sensing data greatly affects the performance of information fusion algorithm. To solve this problem, a Gaussian kernel-based Fuzzy Rough Set fusion algorithm is proposed, since Fuzzy Rough Set theory is an effect tool to model uncertainties of data. For feature reduction a novel index is proposed to evaluate the significance of features, considering both the relevance between features and decisions and the redundancy of features. Thus the most informative features are selected for classification. Experiments with standard test data and real remote sensing data show that the classifier can achieve a high accuracy using feature subset selected by the proposed method than using the full feature set.Index Terms-imperfect information, feature selection, fuzzy rough set, information fusion

Kernel Neighborhood Rough Sets Model and Its Application

“…A Gaussian kernel rough sets model-based feature selection method was discussed in [8]. The information fusion problem of imperfect images has also been studied based on Hu's research [9]. Ghosh et al proposed an efficient Gaussian kernel-based fuzzy rough sets approach for feature selection [10].…”

Target Recognition Based on Rough Set with Multi-Source Information

“…In essence, identification with multi-attribute means the process of integrating multi-source information. In the field of target recognition technology, the main mathematical analysis methods include The Bayes estimation [1], DS evidence theory [2], fuzzy set theory [3][4][5][6][7][8], neural networks [9][10][11][12][13][14][15], artificial intelligence technology [16], rough set theory [17][18][19][20][21], and so on.…”