A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification

Hui He; Xianchuan Yu

doi:10.1117/12.652298

3 November 2005 A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification

Hui He, Xianchuan Yu

Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604408 (2005) https://doi.org/10.1117/12.652298
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

In this paper a performance comparison of a variety of data preprocessing algorithms in remote sensing image classification is presented. These selected algorithms are principal component analysis (PCA) and three different independent component analyses, ICA (Fast-ICA (Aapo Hyvarinen, 1999), Kernel-ICA (KCCA and KGV (Bach & Jordan, 2002), EFFICA (Aiyou Chen & Peter Bickel, 2003). These algorithms were applied to a remote sensing imagery (1600×1197), obtained from Shunyi, Beijing. For classification, a MLC method is used for the raw and preprocessed data. The results show that classification with the preprocessed data have more confident results than that with raw data and among the preprocessing algorithms, ICA algorithms improve on PCA and EFFICA performs better than the others. The convergence of these ICA algorithms (for data points more than a million) are also studied, the result shows EFFICA converges much faster than the others. Furthermore, because EFFICA is a one-step maximum likelihood estimate (MLE) which reaches asymptotic Fisher efficiency (EFFICA), it computers quite small so that its demand of memory come down greatly, which settled the "out of memory" problem occurred in the other algorithms.

Citation Download Citation

Hui He and Xianchuan Yu "A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604408 (3 November 2005); https://doi.org/10.1117/12.652298

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