Automatic acquisition method of ground vegetation conditions based on U-Net and clustering algorithm in remote sensing monitoring

The current agricultural and forestry remote sensing monitoring relies on low-level features to obtain information about the specific conditions of ground vegetation, and the quality of the obtained information is not high. In response to this situation, the study proposes an automatic acquisition method for ground vegetation conditions based on the U-shaped Network (U-Net) and clustering algorithm. The study first employs an improved U-Net model to classify and predict remote sensing images, distinguishing between vegetation areas and non-vegetation areas. Subsequently, the simple linear iterative clustering algorithm is utilized to perform fine-grained segmentation on the remote sensing images. Finally, the classification prediction results are combined with the fine-grained segmentation results to generate label values for each pixel within the segmentation area. This process yields a more precise vegetation distribution map. The outcomes revealed that the classification accuracy of the improved U-Net reached 95.46%, and the fine-grained segmentation accuracy was improved to 89.32%. In practical applications, the information extraction efficiency of this method was relatively fast, reaching 25.4 frames per second. Furthermore, the intersection over union and F1 score of the extracted results were 0.89 and 0.93, respectively. This indicates that while improving the quality of vegetation information acquisition, this method significantly enhances the processing speed, providing efficient and accurate technical support for agricultural and forestry vegetation management.