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Convolutional neural networks (CNNs) have proved to achieve state-of-the-art performance in content-based remote sensing image retrieval (CBRSIR). However, CNNs cannot focus on discriminative features of important objects, resulting in unsatisfactory retrieval performance with complex backgrounds and small objects. We therefore propose a multi-attention fusion network with dilated convolution and label smoothing for CBRSIR. First, a dilated convolutional layer is used to replace the fifth convolutional layer in the network to obtain a large receptive field. Then, a contextual transformer attention (CoT) and an efficient channel attention (ECA) are fused together for spatial-wise and channel-wise discriminative features, respectively. The multi-attention module is embedded between the newly added dilated convolutional layer and the followed average pooling layer. Besides, in order to enhance the differences between the discriminative features of those correct and incorrect classes, label smoothing is used to replace the cross-entropy loss function. Some ablation experiments are conducted on six benchmark datasets. Compared to the baseline AlextNet with the above different modules, the mAP values of the proposed network improve by 14.84% to 24.21%. The results indicate that our network can significantly improve the retrieval performance. In addition, we have also conducted some experiments for network migration and comparison with some recent methods (e.g. a triplet deep metric learning and deep feature learning with latent relationship embedding network). Experimental results illustrate that our network can be effectively migrated to other similar CNN models and can achieve state-of-the-art or competitive results. [ABSTRACT FROM AUTHOR]

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O 3 and PM 2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O 3 , CO is the most important factor for PM 2.5 , and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs. [Display omitted] • CNN coupled with LSTM and attention mechanism for atmospheric predictions. • Proposal outperforms other models, with R2 > 0.9 between predicted and observed data. • Sensitivity analysis identifies factors contributing most to pollutant levels. • Removing variables that contribute little to output improves modeling efficiency. • Key factors remain similar during the whole period and before COVID-19 outbreak. [ABSTRACT FROM AUTHOR]