Abstract: Abstract: Wetland is one of the most important ecosystems, and it has high social benefit, economic benefit and scientific research value. However, wetland resources are bearing a heavy pressure because of various natural and anthropogenic factors. The degradation of the wetland quality and quantity has aroused widespread concerns. To conserve and manage wetland resources, it is important to monitor wetlands and their adjacent uplands. Satellite remote sensing has several advantages, such as wild coverage, saving time and labor, multi-temporal, multi-platform, containing a large amount of information, and so on, when monitoring wetland resources especially in large geographic areas. In early work, the satellite imagery used the visual interpretation for classification, which is still used widely today. The most commonly used computer classification methods are unsupervised classification and supervised classification. However, it is difficult to make great progress on improving the accuracy of remote sensing classification because of "different things with the same spectrums" in wetlands. Spectrum confusion among wetlands seriously restricts the extraction of wetland information and the application of remote sensing technology in the monitoring of the wetland. But the traditional pixel-based methods cannot overcome this difficulty because it only used the spectral features of imagery, ignoring other information that the remote sensing imagery carries, although it has been universally applied in land cover information extraction for many years. In order to over this difficulty and promote the application of remote sensing technology in dynamic monitoring of wetland, a new hybrid classification approach for wetland was proposed in this paper, which combined the object-oriented technology and the tasseled cap transformation method. The new proposed approach was further checked by a case study of wetland extraction based on the HJ-CCD and Landsat ETM (enhanced thematic mapper) remote sensing images in 2010 in the eastern Dongting Lake region. We yielded a better classification result using the new approach. The overall accuracy was 90.02% and the Kappa coefficient was 0.88, which were much higher than that of the traditional pixel-based methods. Meanwhile, this method significantly reduced the disturbance of salt-and-pepper noise, and the results were quite compact and smooth compared with that using other traditional classification methods. A higher accuracy was obtained for the proposed approach for vegetation wetlands including wood wetland, shrub wetland and grass wetland, which was attributed to the full mining of imagery spectral information through the tasseled cap transformation. The accuracy of the hybrid approach was much higher than that of others for river, channel, reservoir and lake whose spectrums were extremely similar. This was mainly because the object-oriented technology could fully utilize spatial and shape information of imagery. Hence, according to the experiment results, the proposed approach combing the object-oriented technology and the tasseled cap transformation is an effective method in wetland extraction using the remote sensing technology and can overcome the difficulty of spectrum similarity, which is mainly attributed to making full use of spatial feature on the basis of exploring the spectral features through the tasseled cap transformation. Meanwhile, we can conclude that Chinese HJ-CCD images are an important data source for monitoring the dynamics of wetland.