Sample weighting constrained energy minimization algorithm

Constrained Energy Minimization (CEM) algorithm is very sensitive to spectral difference of the same object and cannot detect the large targets. We proposed a sample weighting CEM algorithm. Through spectral vector unitization, the errors caused by different environment are decreased, and target recognition accuracy is increased. To decrease the proportion in the sample autocorrelation matrix, we use spectral correlation as a similarity measure to weight the samples. The modified algorithm acquired the satisfied effect for large targets.