高光谱图像分类的ReliefF-RFE特征选择算法构建与应用

doi:10.3964/j.issn.1000-0593(2022)10-3283-08

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Abstract Hyperspectral images are characterized by continuous bands, high dimensionality, large data volume and strong correlation between adjacent bands, which can provide richer detailed information for feature classification. However, there is a lot of redundant information and noise in data, and the direct use of all band features without effective analysis and selection in image classification will lead to low computational efficiency and high computational complexity, and the “Hughes phenomenon” that the classification accuracy may increase and then decrease with the increase of band dimension. In order to quickly extract a subset of features with good recognition ability from hyperspectral images with tens or even hundreds of bands to avoid the “dimensional disaster”. This paper combines the filtered ReliefF algorithm and the wrapped recursive feature elimination algorithm (Recursive feature elimination, RFE) to build the ReliefF-RFE feature selection algorithm, which can be used for feature selection in hyperspectral image classification. The algorithm uses the ReliefF algorithm to quickly eliminate many irrelevant features based on weight thresholds to narrow and optimize the range of feature subsets. The RFE algorithm is used to further search for the optimal feature subsets, and the recursive elimination of the less relevant features and redundant to the classifier in the narrowed feature subsets is performed to obtain the feature subsets with the best classification performance. In this paper, three standard datasets, including the Indian pines dataset, Salinas-A dataset and KSC dataset, are used as experimental data to compare the application effect of the ReliefF-RFE algorithm with ReliefF and RFE algorithms. The results show that the hyperspectral image classification by applying the ReliefF-RFE algorithm has an average overall accuracy (OA) of 92.94%, F-measure of 92.81%, and Kappa coefficient of 91.94%; in the three datasets, the average feature dimension of ReliefF-RFE algorithm is 37% of that of ReliefF algorithm, while the average operation time is 75% of that of the RFE algorithm. It shows that the ReliefF-RFE algorithm can ensure the classification accuracy while overcoming the defects of the filtered ReliefF algorithm, which cannot effectively reduce the redundancy among features and the wrapped RFE algorithm, which has high time complexity and has a more balanced comprehensive performance, which is suitable for feature selection in hyperspectral image classification.

Key words：Hyperspectral image; Feature selection; ReliefF algorithm; RFE algorithm; ReliefF-RFE algorithm

Received: 2021-10-10 Accepted: 2022-01-16

ZTFLH:

TP79

Corresponding Authors: XU Zhang-hua E-mail: fafuxzh@163.com

Cite this article:

XIANG Song-yang,XU Zhang-hua,ZHANG Yi-wei, et al. Construction and Application of ReliefF-RFE Feature Selection Algorithm for Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3283-3290.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)10-3283-08 OR https://www.gpxygpfx.com/EN/Y2022/V42/I10/3283

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