A practical strategy for spectral library partitioning and least-squares identification

This paper proposes a method of partitioning large data libraries into smaller sub-partitions, in such a way that a least-squares-based identification process will be numerically better behaved. An example from a well-known remote sensing spectral library is used to illustrate various seed strategies for the partitioning as well as various assignment strategies. In the example shown seed strategy is relatively unimportant for a library of this size, but there is a substantial improvement in least-squares performance with SVD-based partitioning for both point and interval estimates. Several context-dependent variants of this strategy are also proposed.