The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 13–17, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-13-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 13–17, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-13-2017

  19 Oct 2017

19 Oct 2017

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS

S. Äyrämö, I. Pölönen, and M. A. Eskelinen S. Äyrämö et al.
  • Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

Keywords: Robust, Clustering, Spectral data, Interpolation, K-means, nan-K-spatmed

Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-form solutions. We will compare robust clustering methods on the bands incomplete data cubes to standard K-means with full data cubes.