Improving the Interpretability and Reliability of Regional Land Cover Classification by U-Net Using Remote Sensing Data

The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment. At present, refined land cover data are mainly obtained by manual visual interpretation, which has the problems of heavy workload and inconsistent interpretation scales. Deep learning has greatly improved the automatic processing and analysis of remote sensing data. However, the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification. To improve the efficiency of deep learning-based remote sensing image interpretation, we selected multisource remote sensing data, assessed the interpretability of the U-Net model based on surface spatial scenes with different levels of complexity, and proposed a new method of stereoscopic accuracy verification (SAV) to evaluate the reliability of the classification result. The results show that classification accuracy is more highly correlated with terrain and landscape than with other factors related to image data, such as platform and spatial resolution. As the complexity of surface spatial scenes increases, the accuracy of the classification results mainly shows a fluctuating declining trend. We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene. Based on the results observed in this study, we consider the distinction of interpretability and reliability in diverse ground object types and design targeted classification strategies for different surface scenes, which can greatly improve the classification efficiency. The key achievement of this study is to provide the theoretical basis for remote sensing information analysis and an accuracy evaluation method for regional land cover classification, and the proposed method can help improve the likelihood that intelligent interpretation can replace manual acquisition.