Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification resul...

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Bibliographic Details
Published inInternational journal of remote sensing Vol. 40; no. 9; pp. 3344 - 3367
Main Authors Xie, Fuding, Lei, Cunkuan, Li, Fangfei, Huang, Dan, Yang, Jun
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 03.05.2019
Taylor & Francis Ltd
Subjects
Accuracy
Algorithms
Classification
Computation
Decomposition
Educational institutions
Entropy
FCM algorithm
feature selection
GWO algorithm
hyperspectral image
Hyperspectral imaging
Image classification
Maximum entropy
Methods
Optimization
Spectral bands
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Summary:Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective feature subset from original bands to reduce the dimensionality of the hyperspectral dataset. In this study, a novel unsupervised feature selection method is suggested to remove the redundant features of HSI by feature subspace decomposition and optimization of feature combination. Feature subset decomposition is achieved by the fuzzy c-means (FCM) algorithm. The optimal feature selection is based on the optimization process of grey wolf optimizer (GWO) algorithm and maximum entropy (ME) principle. To evaluate the effectiveness of the proposed method, experiments are conducted on three well-known hyperspectral datasets, Indian Pines, Pavia University, and Salinas. Six state-of-the-art feature selection methods are used to compare with the proposed method. Experimental results successfully confirm the superior performance of our proposal with respect to three classification accuracy indices overall accuracy (OA), average accuracy (AA) and kappa coefficient (κ).
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2018.1541366