Spectral-Spatial Feature Extraction of Hyperspectral Images Based on Propagation Filter

Sensors

Junjun Jiang¹  Xiaoping Fang¹  Xinwei Jiang¹  Zhikun Chen¹  Zhihua Cai¹ 

[1] School of Computer Science, China University of Geosciences, Wuhan 430074, China;

关键词: propagating filter;    hyperspectral image;    spectral-spatial feature extraction;    image classification;   

DOI  :  10.3390/s18061978

来源: DOAJ

【 摘 要 】

Recently, image-filtering based hyperspectral image (HSI) feature extraction has been widely studied. However, due to limited spatial resolution and feature distribution complexity, the problems of cross-region mixing after filtering and spectral discriminative reduction still remain. To address these issues, this paper proposes a spectral-spatial propagation filter (PF) based HSI feature extraction method that can effectively address the above problems. The dimensionality/band of an HSI is typically high; therefore, principal component analysis (PCA) is first used to reduce the HSI dimensionality. Then, the principal components of the HSI are filtered with the PF. When cross-region mixture occurs in the image, the filter template reduces the weight assignments of the cross-region mixed pixels to handle the issue of cross-region mixed pixels simply and effectively. To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the PF. The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification.

【 授权许可】

Unknown