The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 27–30, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-27-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 27–30, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-27-2017

  26 Sep 2017

26 Sep 2017

ASSESSMENT OF NORMALIZATION TECHNIQUES ON THE ACCURACY OF HYPERSPECTRAL DATA CLUSTERING

A. Alizadeh Naeini1, M. Babadi1, and S. Homayouni2 A. Alizadeh Naeini et al.
  • 1Dept. of Geomatics Eng., Faculty of Civil Engineering and Transportation, U. of Isfahan, Iran
  • 2Dept. of Geography, Environment, and Geomatics, U. of Ottawa, Canada

Keywords: K-means clustering, normalization techniques, density based initialization, hyperspectral data

Abstract. Partitioning clustering algorithms, such as k-means, is the most widely used clustering algorithms in the remote sensing community. They are the process of identifying clusters within multidimensional data based on some similarity measures (SM). SMs assign more weights to features with large ranges than those with small ranges. In this way, small-range features are suppressed by large-range features so that they cannot have any effect during clustering procedure. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed images. To address this problem, the feature normalization (FN) can be used. However, since different FN methods have different performances, in this study, the effects of ten FN methods on hyperspectral data clustering were studied. The proposed method was implemented on both real and synthetic hyperspectral datasets. The evaluations demonstrated that FN could lead to better results than the case that FN is not performed. More importantly, obtained results showed that the rank-based FN with 15.7% and 12.8% improvement, respectively, in the synthetic and real datasets can be considered as the best FN method for hyperspectral data clustering.