关键词: 高光谱数据分类, 有限训练样本, 协方差矩阵估计, 粒子群优化算法（PSO）

Abstract: Compared with the multispectral remote sensing，the hyperspectral remote sensing can provide data with higher spectral resolution，and so more accurate classification of land cover is usually achieved.However，when the number of the training sample is equal with or less than the data dimension，the covariance matrices are close to singular or badly scaled and thus the maximum likelihood classifier will be degraded.The re-estimation of covariance matrices is necessary.Most methods of covariance estimation only select any two weighted items among the common covariance matrix，sample covariance matrix and their corresponding transforms，with ignorance of more combined items and their effects.Particle Swarm Optimization（PSO） is introduced to estimate covariance matrix.It investigates all the items through optimizing the weighting parameters.The classification results of hyperspectral data demonstrate that the proposed method is effective.

Key words: hyperspectral data classification, limited training samples, covariance matrix estimation, Particle Swarm Optimization（PSO）