Integrating UAV and Object-based Image Analysis for Rural Residential Environment Information Extraction

The remote sensing of Unmanned Aerial Vehicle (UAV) and Object-Based Image Analysis（OBIA） technologies have advanced increasingly for environmental monitoring in recent years. However, references to the uses of UAV and OBIA for mapping rural residential environment are still scarce in the field of scientific literature. In this study, an integration framework was developed to extract various ground objects in rural residential environment. First, Estimation of Scale Parameter (ESP) tool and expert judgement were used to identify the optimal Segmentation Scale Parameter (SSP). Then, the expert rule-sets and supervised classification algorithms were applied to extract information of ground objects in rural residential environment, respectively. Finally, the performance accuracy was evaluated by using an area-based method. The results indicated that using ESP tool and expert judgment to determine the optimal SSP is feasible. Furthermore, the overall accuracy (OA) is 75.19%, indicating that the rule-based extraction method is not good at extracting all kinds of ground objects in study area. However, Solar Water Heaters (SWHs) were successfully extracted in rural residential environment by using template matching combined with threshold rules and the extraction accuracy can achieve 92%. Moreover, the influences of training samples and features were analyzed on the classification results of the Random Forest (RF), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) classifiers, showing that the RF classifier has the best classification result, with its value of OA reaching 91.34%. The results indicated that the integrate framework is a valuable tool in the extraction of ground objects from rural residential environment.