OBH-RSI: Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland

Abstract

With the deterioration of the environment, it is imperative to protect coastal wetlands. Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method. The object-based hierarchical classification using remote sensing indices (OBH-RSI) for coastal wetland is proposed to achieve fine classification of coastal wetland. First, the original categories are divided into four groups according to the category characteristics. Second, the training and test maps of each group are extracted according to the remote sensing indices. Third, four groups are passed through the classifier in order. Finally, the results of the four groups are combined to get the final classification result map. The experimental results demonstrate that the overall accuracy, average accuracy and kappa coefficient of the proposed strategy are over 94% using the Yellow River Delta dataset.