Abstract: Abstract: High-precision wetland mapping is playing a critical role in wetland ecological protection and fine management. In order to improve traditional classification of wetland, a new wetland classification that coupled with the Fully Convolutional Neural (FCN) network and ensemble learning was proposed in this study. A highly versatile feature extractor, including the convolutional layers and pooling layers in Convolutional Neural Network (CNN), can be used to extract highly abstract deep features in remote sensing images. However, the input image size in many CNN models was 256 × 256 or 299 × 299 pixels, which cannot satisfy the features information extraction of wetland in a large scale. In the FCN, the fully connected layer can be replaced with a convolutional layer, where the images with any size can be accepted for the pixel-by-pixel classification. That was the reason that the FCN was chosen to extract image features. Here, the FCN (SegNet, UNet, and RefineNet) was employed to extract and merge the deep semantic features in the GF-6 images. Since single machine learning was easy to fall into a local optimal solution, with the relatively low generalization ability of unknown samples, ensemble learning can be utilized as multiple base classifiers to predict. A strong ability can be gained to apply for various scenarios with high classification accuracy. Therefore, a stacking ensemble learning model was selected for wetland classification, according to the semantic features derived by FCN, due mainly to a better ensemble effect on the stable classifiers in the task. In some scenarios, the performance of integrated Stacking was better than that of others. However, the performance of integrated stacking may be degraded in some applications. An adaptive stacking was proposed to further improve the stability and generalization ability of current Stacking. The meta-classifier was first determined in the adaptive stacking. The RF in an ensemble classifier was used as the meta-classifier to improve prediction performance. All the base-classifiers were combined freely to train the input dataset, including the Support Vector Machine (SVM), RF, k-Nearest Neighbor (kNN), Logistic Regression (LR), and Naive Bayes (NB). The results showed that the coupled FCN and adaptive Stacking can effectively extract the most types of wetland information, where the overall classification accuracy and Kappa coefficient were 88.16% and 0.85, respectively. The producer accuracy and user accuracy of lakes/pools, mudflat, and sedge were all around 90%, but it was easy to form a misclassification of reed beaches and poplar forest beaches. The main reason was that similar spectral characteristics during the growing season were difficult to distinguish with single-phase remote sensing images. Compared with coupling FCN and single classifier (SVM, RF and kNN), the overall accuracy was improved by 5.31, 4.87, and 5.08 percent points, respectively. Compared with the SVM, RF and kNN, the overall accuracy was improved by 9.68, 7.90, and 10.01 percent points, respectively. Moreover, a higher classification accuracy was achieved, compared with that of SegNet, UNet, or RefineNet. According to the characterization performance of each classifier, ensemble learning can make reasonable selection and reorganization, further to improve the classification accuracy and its generalization ability.