Dimension reduction for hyperspectral image based on the second generation bandelet transform

Xiaoping Du; Hang Chen; Zhengjun Liu; Ming Liu; Xiangzhen Cheng

doi:10.1117/12.2032800

30 August 2013 Dimension reduction for hyperspectral image based on the second generation bandelet transform

Xiaoping Du, Hang Chen, Zhengjun Liu, Ming Liu, Xiangzhen Cheng

Proceedings Volume 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications; 89100S (2013) https://doi.org/10.1117/12.2032800
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China

Abstract

A dimensionality reduction method is proposed by using the second generation Bandelet transform. The redundant components of the hyperspectral cube are firstly partitioned into several subsets. Subsequently the Bandelet coefficients and the geometries flows of the hyperspectral image are generated by performing second generation Bandelet transform. In the follow step, Principal Components Analysis (PCA) is introduced to simplify the redundant data. Finally, the new reduced hyperspectral cube is reconstructed by taking inverse Bandelet transform. Some numerical simulations are made to test the validity and capability of the proposed dimensionality reduction algorithm.

Citation Download Citation

Xiaoping Du, Hang Chen, Zhengjun Liu, Ming Liu, and Xiangzhen Cheng "Dimension reduction for hyperspectral image based on the second generation bandelet transform", Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 89100S (30 August 2013); https://doi.org/10.1117/12.2032800

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KEYWORDS

Principal component analysis

Dimension reduction

Hyperspectral imaging

Numerical simulations

Image segmentation

Wavelet transforms

Data modeling

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