期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：248-257
出版社：Science and Information Society (SAI)
摘要：During the last decade, hyperspectral images have
attracted increasing interest from researchers worldwide. They
provide more detailed information about an observed area and
allow an accurate target detection and precise discrimination of
objects compared to classical RGB and multispectral images.
Despite the great potentialities of hyperspectral technology, the
analysis and exploitation of the large volume data remain a
challenging task. The existence of irrelevant redundant and noisy
images decreases the classification accuracy. As a result,
dimensionality reduction is a mandatory step in order to select a
minimal and effective images subset. In this paper, a new filter
approach normalized mutual synergy (NMS) is proposed in
order to detect relevant bands that are complementary in the
class prediction better than the original hyperspectral cube data.
The algorithm consists of two steps: images selection through
normalized synergy information and pixel classification. The
proposed approach measures the discriminative power of the
selected bands based on a combination of their maximal
normalized synergic information, minimum redundancy and
maximal mutual information with the ground truth. A
comparative study using the support vector machine (SVM)
and k-nearest neighbor (KNN) classifiers is conducted to evaluate
the proposed approach compared to the state of art band
selection methods. Experimental results on three benchmark
hyperspectral images proposed by the NASA “Aviris Indiana
Pine”, “Salinas” and “Pavia University” demonstrated the
robustness, effectiveness and the discriminative power of the
proposed approach over the literature approaches.
关键词：Hyperspectral images; target detection; pixel
classification; dimensionality reduction; band selection;
information theory; mutual information; normalized synergy
