Weighted Feature Line Embedding for Feature Extraction of Hyperspectral Images

One of the most preprocessing steps before the classification of hyperspectral images is supervised feature extraction. Because obtaining the training samples is hard and time consuming¡ the number of available training samples is limited. We propose a supervised feature extraction method in this paper that is efficient in small sample size situation. The proposed method¡ which is called weighted feature line embedding (WFLE)¡ uses the feature line concepts for production of virtual training samples and then¡ uses them for estimation of within-class and between-class scatter matrices. The new idea of WFLE is based on more correction on the non-appropriate and abnormal samples through weighting process in estimation of scatter matrices. The WFLE is compared with some popular and state-of-the-art feature extraction methods such as LDA¡ GDA¡ NWFE¡ NPE¡ LPP and NFLE. The experimental results show the good performance of WFLE in comparison with other methods in small sample size situation.