Remote Sensing (Feb 2023)
Model Construction and System Design of Natural Grassland-Type Recognition Based on Deep Learning
Abstract
As an essential basic function of grassland resource surveys, grassland-type recognition is of great importance in both theoretical research and practical applications. For a long time, grassland-type recognition has mainly relied on two methods: manual recognition and remote sensing recognition. Among them, manual recognition is time-consuming and laborious, and easily affected by the level of expertise of the investigator, whereas remote sensing recognition is limited by the spatial resolution of satellite images, and is not suitable for use in field surveys. In recent years, deep learning techniques have been widely used in the image recognition field, but the application of deep learning in the field of grassland-type recognition needs to be further explored. Based on a large number of field and web-crawled grassland images, grassland-type recognition models are constructed using the PyTorch deep learning framework. During model construction, a large amount of knowledge learned by the VGG-19 model on the ImageNet dataset is transferred to the task of grassland-type recognition by the transfer learning method. By comparing the performances of models with different initial learning rates and whether or not data augmentation is used, an optimal grassland-type recognition model is established. Based on the optimal model, grassland resource-type map, and meteorological data, PyQt5 is used to design and develop a grassland-type recognition system that uses user-uploaded grassland images and the images’ location information to comprehensively recognize grassland types. The results of this study showed that: (1) When the initial learning rate was set to 0.01, the model recognition accuracy was better than that of the models using initial learning rates of 0.1, 0.05, 0.005, and 0.001. Setting a reasonable initial learning rate helps the model quickly reach optimal performance and can effectively avoid variations in the model. (2) Data augmentation increases the diversity of data, reducing the overfitting of the model; recognition accuracies of the models constructed using the augmented data can be improved by 3.07–4.88%. (3) When the initial learning rate was 0.01, modeling with augmented data and with a training epoch = 30, the model performance reached its peak—the TOP1 accuracy of the model was 78.32% and the TOP5 accuracy of the model was 91.27%. (4) Among the 18 grassland types, the recognition accuracy of each grassland type reached over 70.00%, and the probability of misclassification among most of the grassland types was less than 5.00%. (5) The grassland-type recognition system incorporates two reference grassland types to further improve the accuracy of grassland-type recognition; the accuracy of the two reference grassland types was 72.82% and 75.01%, respectively. The recognition system has the advantages of convenient information acquisition, good visualization, easy operation, and high stability, which provides a new approach for the intelligent recognition of grassland types using grassland images taken in a field survey.
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