Supervised Neighborhood Preserving Embedding Feature Extraction of Hyperspectral Imagery

Abstract

Abstract:

Hyperspectral-image feature extraction is important for image classification. In this paper, A novel hyperspectral remote sensing imagery feature extraction algorithm called discriminative supervised neighborhood preserving embedding (DSNPE) is proposed for supervised linear feature extraction. DSNPE can preserve the local manifold structure and the neighborhood structure. What’s more, for each data point, DSNPE aims at pulling the neighboring points with the same class label towards it as near as possible, while simultaneously pushing the neighboring points with different labels away from it as far as possible. Numerical experiments in three real hyperspectral-image datasets are reported to illustrate the out performance of DSNPE when compare DSNPE with a few competing methods, such as PCA, NWFE, LPP and NPE.

Key words: hyperspectral-image, feature extraction, Classification

CLC Number:

TP751

References

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