Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images

作者：

Highlights：

• A segmented minimum noise fraction (MNF) transformation is proposed for efficient feature extraction of hyperspectral images (HSIs).

• The proposed method significantly reduces the transformation time in comparison with the conventional MNF.

• The class separability of the extracted features is improved.

• The extracted features by SMNF even exhibit higher classification accuracy compared with the PCA or MNF.

摘要

Highlights•A segmented minimum noise fraction (MNF) transformation is proposed for efficient feature extraction of hyperspectral images (HSIs).•The proposed method significantly reduces the transformation time in comparison with the conventional MNF.•The class separability of the extracted features is improved.•The extracted features by SMNF even exhibit higher classification accuracy compared with the PCA or MNF.

论文关键词：Feature extraction,Hyperspectral images (HSIs),Minimum noise fraction (MNF)

论文评审过程：Received 2 August 2014, Revised 13 March 2015, Accepted 13 April 2015, Available online 22 April 2015, Version of Record 17 June 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.04.013