Clustering Incomplete Spectral Data with Robust Methods
Äyrämö, S., Pölönen, I., & Eskelinen, M. (2017). Clustering Incomplete Spectral Data with Robust Methods. In E. Honkavaara, B. Hu, K. Karantzalos, X. Liang, R. Müller, E. Nocerino, . . . , & P. Rönnholm (Eds.), ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions (pp. 13-17). International archives of the photogrammetry, remote sensing and spatial information sciences, Volume XLII-3/W3. International Society for Photogrammetry and Remote Sensing. doi:10.5194/isprs-archives-XLII-3-W3-13-2017
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences;Volume XLII-3/W3
© Authors 2017. This is an open access article distributed under the terms of a Creative Commons License.
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.
PublisherInternational Society for Photogrammetry and Remote Sensing
Is part of publicationISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions