Deep learning has achieved great success in many computer vision tasks, such as recognition, image enhancement and image compression. However, it is difficult to use a residual network that can efficiently consider the characteristics of multispectral images for multispectral image compression. In this paper, a novel end-to-end multispectral image compression framework based on a weighted feature channel residual network is proposed to efficiently remove the spatial and spectral redundancy of multispectral images by extracting the importance of each channel. The multispectral image compression framework includes a forward coding network, a rate-distortion optimizer, a quantizer/inverse quantizer, an entropy encoder/decoder and an inverse decoding network. In the encoder, multispectral images are directly fed into the forward coding network, and the main spectral and spatial features of the multispectral images are extracted by the residual block. Additionally, the weighted feature channel module can explicitly model the relationship between feature channels when extracting features from multispectral images and adaptively allocate different weights for each feature channel through training. The rate-distortion optimizer is added to make the main features compact. Then, the intermediate feature data are quantized and encoded by lossless entropy coding to obtain a code stream. In the decoder, the code stream is approximately restored to the intermediate features through the entropy decoder and the inverse quantizer. Then, the intermediate features are reconstructed to the multispectral images by the inverse decoding network. Experimental results on 7-band multispectral images of the Landsat 8 satellite and 8-band multispectral images of WorldView-3 satellite demonstrate that the proposed algorithm can achieve a better PSNR than conventional 2D schemes (which are JPEG2000 and JPEG in this paper) and 3D scheme (which is 3D-SPIHT in this paper) and can effectively preserve more spectral information of multispectral images.

深度学习在许多计算机视觉任务（例如识别，图像增强和图像压缩）中都取得了巨大的成功。然而，难以使用能够有效地考虑多光谱图像的特性的残差网络用于多光谱图像压缩。本文提出了一种基于加权特征通道残差网络的端到端多光谱图像压缩框架，通过提取每个通道的重要性有效地消除了多光谱图像的空间和光谱冗余。多光谱图像压缩框架包括前向编码网络，速率失真优化器，量化器/逆量化器，熵编码器/解码器和逆解码网络。在编码器中，多光谱图像直接馈入前向编码网络，残差块提取多光谱图像的主要光谱和空间特征。此外，当从多光谱图像中提取特征时，加权特征通道模块可以显式建模特征通道之间的关系，并通过训练为每个特征通道自适应分配不同的权重。添加了速率失真优化器以使主要功能紧凑。然后，通过无损熵编码对中间特征数据进行量化和编码以获得代码流。在解码器中，通过熵解码器和逆量化器将代码流近似恢复到中间特征。然后，通过逆解码网络将中间特征重建为多光谱图像。