An Iterative Random Training Sample Selection Approach to Constrained Energy Minimization for Hyperspectral Image Classification

Iterative constrained energy minimization (ICEM) has shown success in classification. However, a drawback suffered from ICEM is its requirement of complete ground truth to calculate class means. This letter develops an iterative selection of training samples to extend ICEM with two versions: iterative fixed training sampling constrained energy minimization (CEM) (IFTS-CEM) which uses a fixed training sample set throughout the entire iterative process and iterative random training sampling CEM (IRTS-CEM) which uses a random training sampling (RTS) at each iteration. The experimental results demonstrate that IRTS-CEM performs better than IFTS-CEM and also comparable to ICEM.