Abstract
A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.
Highlights
The hyperspectral sensors capture many narrow spectral bands by different wavelength
To address the aforementioned issues, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection
In the proposed band selection method, before solving shared nearest neighbor matrix, the distances between each band image and other band images are calculated, the first K distances are obtained by ascending order
Summary
The hyperspectral sensors capture many narrow spectral bands by different wavelength. Feature selection has been a hot topic in the field of machine learning [2,3,4], which is viewed as an effective measure in HSI analysis for dimensionality reduction It removes some redundant information and can obtain satisfactory results in comparison to the raw data. Band selection is a form of feature selection, in which a band subset with low correlation and large information content is selected from all hyperspectral bands to represent the entire spectral bands It can quickly implement subsequent analysis on hyperspectral data set, including change detection [5], anomaly detection [6], and classification [7,8]
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