Wetland Classification Using Deep Convolutional Neural Network

The synergistic use of spatial features with spectral properties of satellite images enhances thematic land cover information. This study aims to address the lack of high-level features by proposing a classification framework based on convolutional neural network (CNN) to learn deep spatial features for wetland. In particular, a CNN model was used for classification of remote sensing imagery with limited number of training data by fine-tuning of a preexisting CNN (AlexNet). The classification results obtained by the deep CNN were compared with those based on well-known ensemble classifiers, namely Random Forest (RF), to evaluate the efficiency of CNN. Experimental results demonstrated that CNN was superior to RF for complex wetland mapping even by incorporating the small number of input features (i.e., 3 features) for CNN compared to RF. The proposed classification scheme serves as a baseline framework to facilitate further scientific research using the latest state-of-art machine learning tools for processing remote sensing data.