Thematic mapping is one of the major application fields of remote sensing techniques. Due to the wide use of the thematic data derived from remotely sensed data by either visual interpretation or automatic classification, data quality of the thematic data was concerned about. Uncertainty in the classified remotely sensed data is one of the most important elements of the data quality. In this paper, the development of the methods for assessment of uncertainty in remotely sensed data classification was overviewed, existing methods and the uncertainty measurements were reviewed and categorized into error matrix based methods, fuzzy set based methods, methods of assessment at pixel scale and other methods. Advantages and the problems remained in every kind of method were analyzed. Future research was suggested that, from the perspective of theoretic study, the uncertainty assessment method at pixel scale and the uncertainty measurements independent on the classifier should be developed; from the perspective of application, attentions should be paid on the optimal spatial sampling and the standardization of uncertainty assessment procedures.