Research on remote sensing image carbon emission monitoring based on deep learning

Abstract

Carbon emission monitoring is the key to achieving global emission reduction. This paper proposes a carbon emission identification method based on remote sensing images. Based on the regional satellite remote sensing image data set, the convolutional neural network model is used to extract the carbon emission characteristics of the region. The multi-model fully connected layer fusion algorithm and gamma correction data augmentation strategy are designed to analyze the accuracy of carbon emission monitoring. The results show that the results will be different when the image can not be preprocessed. In the case of Gamma correction, the model's accuracy is affected, but the robustness of the model is improved. The single-model local image cascade fusion algorithm has higher environmental adaptability than the single-model non-cascade algorithm. The recognition accuracy of the multi-model fusion algorithm is more than 7% higher than that of the single-model local image cascade fusion algorithm. The fully connected layer fusion accuracy is more than 1% higher than that of the feature map fusion, and its recognition accuracy for the data set reaches 87.21% and 88.35%, respectively.