Factor analysis and pattern decomposition method

Motomasa Daigo

doi:10.1117/12.654889

3 November 2005 Factor analysis and pattern decomposition method

Motomasa Daigo

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 604317 (2005) https://doi.org/10.1117/12.654889
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Pattern decomposition method (PDM) may be thought to be a type of spectral mixture analysis in which each pixel is expressed as the linear sum of fixed, standard spectral patterns. The usage of fixed standard spectral patterns makes possible the comparison of data from different time and also from different sensors with the same criteria. In the development of the PDM, I introduced new point of view to interpret the PDM. It is multi-dimensional analysis. In a sense, the standard patterns in the PDM are thought as a kind of principal axes in n-dimensional space but have physical meaning. To make it possible, the PDM adopts an oblique coordinate system. The standard patterns are the axes of the coordinate system. There is another mathematical analysis method that uses oblique coordinate system. It is factor analysis method. In factor analysis, there is an algorithm that extracts meaningful factors in oblique coordinate system. In this paper, I apply this algorithm to Landsat/TM data and show the obtained factors are quite similar to the three standard patterns of the PDM.

Citation Download Citation

Motomasa Daigo "Factor analysis and pattern decomposition method", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604317 (3 November 2005); https://doi.org/10.1117/12.654889

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