Objective In the task of remote sensing image scene classification with complex spatial structures and geographical layouts although deep convolutional neural networks CNNs have better classification performance they often have high complexity and are not suitable for mobile or embedded devices. Therefore a new lightweight hourglass dense network LHD-NET was proposed to achieve a good balance between classification accuracy and model complexity. Methods Firstly shallow information was extracted through a shallow mixed downsampling structure with a feature compensation mechanism. This structure can reduce the number of parameters in subsequent layers while ensuring sufficient information extraction thus improving performance while keeping the model lightweight. Then dense connections were used between hourglass structures to improve feature reuse and to some extent avoid gradient disappearance which promoted information transfer. Finally a convolutional layer feature with high semantic information was used to guide multi-layer feature aggregation so as to improve the performance of the classifier. Meanwhile during the training process the cross-entropy loss function based on label smoothing was employed to smooth the true labels, which could effectively improve robustness and alleviate overfitting compared with ordinary cross-entropy loss functions. Results Experimental results showed that the model achieved significant classification performance with only 5. 4 M parameters obtaining average classification accuracies of 99. 19% 97. 75% and 92. 38% on three publicly available remote sensing datasets namely UC Merced Land-Use SIRI-WHU and NWPU-RESISC45 respectively. Conclusion Experimental results demonstrate that the proposed model can achieve good classification performance with a small number of parameters. Compared with deep neural networks the proposed model significantly reduces the number of model parameters while maintaining high classification accuracy providing certain reference values for remote sensing image scene classification tasks and model lightweighting.