This paper analyses the effect of training samples on supervised classification of remote sensing data, proposes a theory and method for purincation of training samples, which uses spectral and spatial information to remove the undesirable sample pixels. An example shows that divergence between classes, goodness of fit to Gaussian distributinn and classification accuracy can be improved after purification of training samples.
吴健平, 杨星卫. 遥感数据监督分类中训练样本的纯化[J]. 国土资源遥感, 1996, 8(1): 36-41.
Wu Jianping, Yang Xingwei. PURIFICATION OF TRAINING SAMPLES IN SUPERVISED CLASSIFICATION OF REMOTE SENSING DATA. REMOTE SENSING FOR LAND & RESOURCES, 1996, 8(1): 36-41.
[4] Congaltion R. G. etc. . Assessing Lands at classification accuracyusing discrete multivariate analysisstatis ticaltechniques. PE&RS. 1983, 49(12):1671-1678.