A Convolution neural network model with adaptive learning rate and its application-a case study of remote sensing classification of coastal wetland

Coastal wetlands are important ecosystems.To monitor the wetland types distribution of the coastal wetland is of great significance for protecting and utilizing coastal wetlands.The learning rate in the traditional convolution neural network (CNN) model is a fixed value that is manually set.In this paper, a CNN model with adaptive learning rate is proposed, and the optimal value of the learning rate is automatically calculated by using the cost function as the objective function, which makes the CNN model adaptive.Based on the CHRIS hyperspectral remote sensing image data of the Yellow River estuary coastal wetland, the CNN model classification method proposed in this paper is validated and optimized.The experimental results show that:The adaptive learning rate CNN model has the highest overall classification accuracy in the interval of0, 1 for different learning rate search interval, which means that it is necessary to fine tune the interval0, 1 only to ensure better classification accuracy in the process of learning optimization.For the different the initial learning rate, the accuracy and stability of the adaptive learning rate CNN model are higher than those of the traditional CNN model, indicating that the model proposed in this paper is less sensitive to the initial value.In the case of reducing the number of training samples, the stability of the two model classification accuracy have different degrees of reduction.However, the adaptive learning rate CNN model has high stability under the condition that the training sample is more than 1.35% of the total sample, which indicates that the model proposed in this paper has a certain ability to adapt to the small sample.