CLODD based band group selection
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of "blocklike" structure in a dissimilarity matrix (CLODD algorithm) In particular, the iVAT algorithm allows for identification of non contiguous band groups. Experiments are conducted on a benchmark data set and results are compared to existing algorithms based on hierarchical and c means clustering. Our results demonstrate the effectiveness of the proposed method.
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)