A Multilevel Domain Similarity Enhancement Guided Network for Remote Sensing Image Compression

Remote sensing image compression networks aim to enhance the similarity between the input image and the reconstructed image. The current network rarely considers the potential relationship between the compression features of different levels and the reconstruction features of the corresponding levels, which limits the improvement of remote sensing image compression performance. In this article, a concept of multilevel domain similarity is first proposed, which fully develops the multilevel domain similarity between the encoding and decoding processes to improve the quality of reconstructed images. On this basis, a multilevel domain similarity enhancement guided network (MDSNet) is proposed for remote sensing image compression. First, an efficient compression baseline network (BaselineA) was proposed, which realizes efficient image compression with low computational complexity. Second, a multilevel domain similarity enhancement module (MDEM) was designed, which improved the quality of the reconstructed image by enhancing the multilevel domain similarity. Third, a global information-enhanced attention module (GIE-AM) was constructed to enhance channel features and global features. Finally, under the guidance of the total loss (LossTotal), which is constructed by the proposed MDEM loss (MDEM-Loss), an effective compression was implemented by the whole network for remote sensing image compression. Experimental results show that compared with some advanced compression models, the proposed MDSNet can significantly improve compression performance with lower computational complexity. In addition, the reconstructed images obtained by the proposed method can provide better classification performance, which further proves that the proposed MDSNet helps to preserve more important features of remote sensing images during the compression process.