LAUSR

In recent years, the impressive feature representation capabilities of deep learning have opened up new possibilities for image compression. Most of the existing learning-based image compression techniques rely on convolutional neural networks (CNNs) to obtain local feature representations of the input image using moving windows. However, the convolutional kernel of CNNs only considers local spatial relationships in the perceptual field, while ignoring long dependencies in the features. This results in incomplete compression of the latent representation. Similar features in remote sensing (RS) images are more abundant and widely available, and thus, the inconvenience of CNNs is more obvious. To address this problem, we propose a hybrid attention compression network (HACN) for RS images, which can exploit long dependencies in the latent representation to achieve a more compact bitstream. Specifically, the residual attention module (RAM) and graph attention module (GAM) are attached to the network. This hybrid attention mechanism (HAM) helps the encoder extract spatial and cross-channel long dependencies in the feature transformation process, which in turn improves the rate-distortion metric. Meanwhile, regarding the computational cost of the network, we also propose a light GAM, which greatly reduces the burden of compression. The experimental results demonstrate that the proposed method achieves satisfactory rate-distortion performance, compared to conventional and CNN-based methods.