Sparse Tensor-Based Dimensionality Reduction for Hyperspectral Spectral-Spatial Discriminant Feature Extraction

作者：Liu, Zhi; Tang, Bo; He, Xiaofu; Qiu, Qingchen; Wang, Hongjun^*

来源：IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1775-1779.

DOI：10.1109/LGRS.2017.2734960

• Dimensionality reduction (DR)
• hyperspectral image (HSI)
• sparse factorization
• spectral-spatial feature extraction
• tensor discriminant analysis

This letter explores a spectral-spatial tensor-based dimensionality reduction (DR) method to cope with hyperspectral image (HSI) feature extraction and classification. This method uses the Gabor filter banks as the bias spectral-spatial feature hybrider and further integrates the tensor-based alignment strategy for the discriminant locality with sparse factorization by extracting optimal spectral-spatial features and simultaneously maintaining structural relevance. Comparative experimental results with two real HSIs demonstrate that the proposed DR method has a considerable advantage over other traditional feature extraction methods.

• 出版日期2017-10
• 单位山东大学