Fuzzy geospatial objects - based wetland remote sensing image Classification: A case study of Tianjin Binhai New area

Wetland system is one of the most important ecosystems on the earth's surface. It is significant important to monitor wetland ecosystem using remote sensing technology. However, the complexity, fuzziness, and spatial heterogeneity of wetlands increase the difficulty of wetland classification, leading to the problem that the classification accuracy is not high enough to satisfy the needs of in-depth research. At present, the classification of wetlands is mainly based on pixel based- and image object based- methods. Addressing the problems of traditional pixel based- and image object based- methods, this study proposes to utilize fuzzy geospatial objects to express wetland objects. By synthesizing the spectral features, shape features, texture features, fuzziness and other features of wetland objects, a hierarchical classification method based on fuzzy geospatial objects is proposed. Taking Tianjin Binhai New Area as the study area, Sentinel-2 satellite remote sensing images are utilized for verification. The main contents of this study and its results are as follows: (1) Extract the fuzzy geospatial objects of wetlands and construct the classification feature sets. (2) To simplify the classification problem, a hierarchical classification framework based on optimizing multiple attributes using Random Forest is proposed. By this method, the problems of difficulty in distinguishing wetlands and low classification accuracy caused by similarity of spectral features of wetland objects in the traditional single layer classification method are solved. Three experiments are designed in the study to verify the effects of the fuzzy geospatial objects of wetlands and the hierarchical classification method on the classification accuracy of wetlands, respectively. The results show that the overall accuracy and Kappa coefficient of the proposed hierarchical wetland classification method based on fuzzy geospatial objects are 94.35% and 0.899, respectively, which are 12.35% and 0.183 higher than those of the traditional image object based- methods.