Terrain Image Classification Based on Vision Transformer Deep Learning Algorithm

Terrain image classification is an important research direction in the field of remote sensing and computer vision, aiming to realize automatic recognition and classification of different landform features through the analysis and processing of terrain images. In this paper, a deep learning algorithm based on Vision Transformer (ViT) is used to classify terrain images, and the performance of the algorithm in this task is systematically evaluated. In the process of model construction, we first imported the Vision Transformer model and made corresponding parameter Settings to ensure its adaptability and effectiveness. After training, it is observed that the loss function of the training set decreases from the initial value of 2.84 to 0.35, a decrease of 2.49, indicating that the model tends to converge in continuous optimization. At the same time, the accuracy is also significantly improved, from 55.9% to 86.8%, an increase of 30.9%, showing the enhancement of the model's learning ability. For the validation set, loss also decreased from 0.78 to 0.47, a decrease of 0.31, while accuracy increased from 60.1% to 83.5%, an increase of 23.4%. These results further prove the good performance of the model on different data sets and its convergence trend. In addition, through the evaluation of the test set, we get more specific performance indicators: The accuracy of the terrain image classification model based on Vision Transformer on the test set reaches 89.9%, the Precision is 0.9615, the Recall is 0.9494, and the F1-score is 0.9554. These indicators show that the model not only has high classification accuracy, but also performs well in generalization ability. To sum up, this research demonstrates the effectiveness of Vision Transformer deep learning algorithm in terrain image classification, and provides a new solution idea and method for related fields. Through continuous optimization and adjustment, the algorithm is expected to achieve more extensive promotion in practical applications, and bring positive impact on geographic information system, environmental monitoring and other fields..