Research on Remote Sensing Image Land Feature Segmentation Based on Deep Learning
Feature analysis plays a crucial role in village and town construction,providing critical information for decision-making support in planning,management,and monitoring.With the development of deep learning technology,semantic segmentation methods based on deep learning have shown strong potential in the field of terrain analysis.This article focuses on this issue and investigates seven current deep learning based semantic segmentation methods.These methods have been extensively tested and validated on actual datasets.The experimental results indicate that each model exhibits different advantages in land feature segmentation tasks.In order to further improve the accuracy and robustness of terrain analysis,this paper proposes an ensemble learning method that weights and fuses the prediction results of multiple models.This method has achieved significant performance improvement,with a pixel classification accuracy of up to 89.64%.This indicates the potential application of ensemble learning in terrain analysis,providing more reliable technical support for village and town construction.This study provides valuable practical experience for the application of deep learning in the field of terrain analysis,and provides important references for future related research and applications.
feature analysisAI plus urban and rural planningsemantic segmentation
万方数据
维普
