원문정보
초록
영어
Denoising and Dimensionality Reduction (DR) are key issue to improve the classifiers efficiency for Hyper spectral images (HSI). The multi-way Wiener filtering recently developed is used, Principal and independent component analysis (PCA; ICA) and projection pursuit (PP) approaches to DR have been investigated. These matrix algebra methods are applied on vectorized images. Thereof, the spatial rearrangement is lost. To jointly take advantage of the spatial and spectral information, HSI has been recently represented as tensor. Offering multiple ways to decompose data orthogonally, we introduced filtering and DR methods based on multilinear algebra tools. The DR is performed on spectral way using PCA, or PP joint to an orthogonal projection onto a lower subspace dimension of the spatial ways. We show the classification improvement using the introduced methods in function to existing methods. This experiment is exemplified using real-world HYDICE data. Multi-way filtering, Dimensionality reduction, matrix and multilinear algebra tools, tensor processing.
목차
1. Introduction
2 Matrix algebra-based DR methods
2.1 HSI representation
2.2 Principal component analysis based DR approach
2.3 Independent component analysis based DR approach
2.4 Projection pursuit based DR approach
3. Tensor representation and some properties
4 Multilinear algebra-based DR method
4.1 Tensor formulation of PCAdr and PPdr
4.2 Multilinear algebra and PCA-based DR method
4.3 Multilinear algebra and PP-based DR method
5. Experimental results
5.1 Experiment on simulated data
5.2 Experiment on real-world data
6. Conclusion
References