高光谱成像
特征选择
离散余弦变换
计算机科学
特征(语言学)
相互信息
人工智能
冗余(工程)
算法
模式识别(心理学)
数学
数据挖掘
图像(数学)
哲学
语言学
操作系统
作者
Yanfei Zhong,Ailong Ma,Yanfei Zhong,Xin Hu,Liangpei Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2020-05-01
卷期号:58 (5): 3601-3618
被引量:38
标识
DOI:10.1109/tgrs.2019.2958812
摘要
Feature selection is an effective way to reduce the data dimensionality of hyperspectral imagery and obtain a better performance in the subsequent applications, such as classification. The ideal approach is to obtain the optimal tradeoff between two criteria for hyperspectral image feature selection: 1) information preservation and 2) redundancy reduction. However, constructing a hyperspectral feature selection model for the above two criteria is difficult due to the complexity of hyperspectral imagery. Although evolutionary multiobjective optimization methods have been recently presented to simultaneously optimize the above criteria, they cannot control the global exploration versus local exploitation capabilities in the search space for the hyperspectral feature selection problem. Thus, in this article, a novel discrete sine cosine algorithm (SCA)-based multiobjective feature selection (MOSCA_FS) approach is proposed for hyperspectral imagery. In the proposed method, a novel and effective framework of multiobjective hyperspectral feature selection is designed. In the framework, the ratio between the Jeffries-Matusita (JM) distance and mutual information (MI) is modeled to minimize the redundancy and maximize the relevance of the selected feature subset. In addition, another measurement - the variance (Var) - is applied for maximizing the information amount. Furthermore, to resolve the discrete hyperspectral feature selection problem, a novel discrete SCA is first proposed, which enhances the selection of the ideal feature subset. The effectiveness and universality of the proposed method was verified by experiments with ten University of California at Irvine (UCI) data sets, five hyperspectral image data sets, and one spectral data set of typical surface features.
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