Abstract: In high-resolution remote sensing image retrieval, due to the complex image content and rich detailed information, it is difficult for the features extracted by a convolutional neural network to effectively express the salient information of the image. In response to this issue, a self-attention module based on cascade pooling is proposed to improve the feature representation of convolutional neural networks. Firstly, a cascade pooling self-attention module is designed, and the self-attention module can learn key salient features of images on the basis of establishing semantic dependencies. Cascade pooling uses max pooling based on a small region, and then adopts average pooling based on the max pooled feature map. The cascade pooling is exploited in the self-attention module, which can keep important details of the image while paying attention to the salient information of the image, thereby enhancing feature discrimination. After that, the cascade pooled self-attention module is embedded into the convolutional neural network for feature optimization and extraction. Finally, in order to further improve the retrieval efficiency, supervised hashing with kernels is applied to reduce the dimensionality of features, and then the obtained low-dimensional hash code is utilized for remote sensing image retrieval. The experimental results on the UC Merced, AID and NWPU-RESISC45 data sets show that the proposed method can improve the retrieval performance effectively.