Deriving thematic maps by classifying remotely sensed data was a major application fields of remote sensing techniques. The most often used classifiers in classification process of remotely sensed data include various statistical classifiers and artificial neural networks. Comparisons among these classifiers found no classifier as "panacea". While most efforts were made to develop new classifiers for more accurate classification results

to fully exploit the potentials of the existing classifiers by combining multiple existing classifiers is an effective way in many fields of pattern recognition applications. In this paper

the standard multiple classifier combination method was used for land cover mapping using remotely sensed data. Landsat TM data in Lanier Lake was used as an experimental data. Land cover maps were derived by combining classifiers at abstract level with same training features

combining classifiers at abstract level with different training features and by combining classifiers at measurement level respectively. Classification accuracies of these maps were compared with those of classifiers combined. Results showed that for all classifiers combination methods

the classification accuracies were improved. Advantages and drawbacks of every method of classifiers combination were analyzed and further study in combining multiple classifiers for remotely sensed data classification was suggested.