Remote Sensing Image Road Recognition and Detection Technology Based on Machine Learning

This article focuses on the research of remote sensing image road recognition technology based on machine learning, in order to explore more effective road detection methods. By introducing advanced machine learning algorithms and combining the characteristics of remote sensing images, this study proposes a new framework based on the Convolutional Neural Network Long Short Term Memory (CNN-LSTM) fusion model, and verifies its effectiveness through experiments. The experimental results show that compared with other methods, the fusion model in this study has significantly improved recognition accuracy and efficiency, and can provide strong technical support for urban planning, traffic management and other fields. Four experiments were designed during the experimental phase to evaluate the performance of each model under different conditions. In the benchmark model performance experiment, the AUC (Area Under the ROC Curve) value of the CNN-LSTM fusion model was 0.95. In the recognition experiments of terrain with different complexities, the CNN-LSTM model achieved recognition accuracy of 80%, 90%, and 70% in urban, rural, and forest areas, respectively. In the third recognition experiment of images with different resolutions, the accuracy of the CNN-LSTM fusion model was 80%, 85%, and 90% at low, medium, and high resolutions, respectively. In the final model robustness evaluation experiment, the CNN LSTM fusion model showed the smallest decrease in accuracy when faced with different noise levels. In the experimental data conclusion, the CNN-LSTM algorithm has excellent superiority in remote sensing image road recognition tasks. It not only has high accuracy, but also has strong adaptability and robustness, providing strong technical support for future applications.