Landscape Classification Method Using Improved U-Net Model in Remote Sensing Image Ecological Environment Monitoring System

Aiming at the problems of low classification accuracy and time-consuming properties in traditional remote sensing image classification methods, a remote sensing image classification method of ecological garden landscape based on improved U-Net model is proposed. Firstly, the remote sensing images of ecological garden landscape are collected by s185 multirotor unmanned aerial vehicle (UAV) system and preprocessed by min-max standardization and data enhancement. Then, the asymmetric convolution block and attention mechanism are used to improve the U-Net model to form the Att-Unet network model, so as to overcome the problems of easy overfitting of the model and incomplete small target detection. Finally, the fully connected conditional random field is introduced into the classification postprocessing to refine the segmentation results. Based on the Keras learning framework, the proposed method is experimentally demonstrated. The results show that the recall, precision, F1 value, and accuracy of the proposed method in the remote sensing image of ecological garden landscape are 0.854, 0.801, 0.836, and 0.982, respectively, and the classification test time is 8.9s. The overall performance is better than other comparison methods, which can provide theoretical support for the dynamic monitoring of the development of ecological garden.