Wetland hydrophytes are an important part of wetland resource, and the change of their number and extent have a direct impact to the wetland habitat quality. So the quantitative study of wetland hydrophytes change is of great significance for the protection of wetland change. We found that wetland hydrophytes classification using remote sensing images is less detail oriented, the application of object-oriented classification method applying medium-resolution remote sensing image in wetland hydrophytes classification is not sufficient. In this research, we applied object-oriented classification method to extract the wetland hydrophytes types of Wild Duck Lake Wetland Natural Reserve in Beijing, China. The medium-resolution Landsat TM and ETM+ multi-spectral remote sensing images which the resolution is 30 meter acquired in August, 2002 and 2008 were selected as the data source of the classification. The reason that the August imageries were selected because they show the vigorous growing season of wetland hydrophytes, and have strong spectral reflectance and plentiful spectral information. The results show: (a) during the extraction process, using Principal Component Transformation (K-L Transformation) and Tasseled Cap Transformation (K-T Transformation) to separate the key information from background noise can reduced data redundancy, the Principal Component Transformation only retains the first component of the largest eigenvalue, Tasseled Cap Transformation component outputs the first three features; (b) the method can correlate different bands; (c) the method can increase the diversity of spectral and spatial information between wetland hydrophytes and other surface features on land or water. Meanwhile, after analyzing of wetland hydrophytes, a typical wetland hydrophytes spectral characteristic curve was drawn comprehensively. Through the field spectral characteristics analysis, NDVI and NDWI were applied for classification, and characterizing the bands or band combinations were constructed. Then using decision tree method for data analysis, the automatic classification of wetland hydrophytes could be accomplished through the appropriate membership function and threshold range as constructing the decision tree. After the automatic classification, make use of manual editing module in eCognition, we combined spectral, size, texture and other structural information of features, and assigned the polygon objects to the correct category. This approach increased the classification accuracy. By making use of classification stability and classification accuracy test based on the polygon objects, the results of overall accuracy of 2002 and 2008 classifications were 86.5% and 85.44%, respectively. And classification stability standard deviation is less than 0.25, the average stability is greater than 0.84.The research indicated that medium- resolution TM image can meet the needs of wetland hydrophytes extraction. Moreover, because of the nature of high spectral resolution, plenty of vegetation cover information, relatively lower cost, and longer time accumulation of imageries, TM and ETM+ can be used as the main data source of wetland hydrophytes extraction and dynamic monitoring, in particular using the data from the last two decades. It can not only provide a more intuitive, scientific and accurate basis for the wetland hydrophytes protection in Wild Duck Lake area, but also provide a new way for other areas of wetland aquatic vegetation extraction and change detection.