Encoder-decoder with atrous separable convolution for semantic image segmentation. Notes: OAA means the overall attribution accuracy ELA means the edge localization accuracy SAV means the stereoscopic accuracy verification calculated from OAA and ELA – means that there is no corresponding land cover type in the test areaĪdam H, Chen L C, Papandreou G et al., 2018. 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. 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. We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene. As the complexity of surface spatial scenes increases, the accuracy of the classification results mainly shows a fluctuating declining trend. 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. 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. However, the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification. Deep learning has greatly improved the automatic processing and analysis of remote sensing data. At present, refined land cover data are mainly obtained by manual visual interpretation, which has the problems of heavy workload and inconsistent interpretation scales. The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment.
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