Accuracy assessment is an indispensable step in the process of classification of remotely sensed data.The common method is carried out through confusion matrix established on reference data

which has three deficiencies: the heavy workload

inability to guarantee the complete correctness of reference data

the cost of reduction error resulting in the increase of workload.In remotely sensed imagery

the feature vector belonging to one category obeys the normal distribution.Based on this hypothesis and statistic theory

a new method is proposed established on category distribution.The reference data is unnecessary for proposed method.For the supervised classification

the workload is extremely little.The key to the proposed method is that the category population can pass the hypothesis test of a certain distribution

in this case

producer’s accuracy can be figured out easily.Given the number of the category population

the user’s accuracy can be figured out too

and then the overall accuracy can be estimated by user’s accuracy and area proportions of all categories after classification.Finally

the proposed method in this paper was applied to image classification for Zhengzhou city as an example.The result shows: if the distribution of category population can be given

producer’s accuracy obtained by common method and proposed method completely conforms in the perspective of statistics.