Image compression usually includes two important operations: compression and decompression. The compression process includes the operation of discarding information, while the decompression process is to retrieve the lost information. In order to make the decompressed image more similar to the original image, the classic compression methods generally adopt the process of approximately reversible compression and decompression. Inspired by the symmetric structure in classic compression methods, we propose a new symmetrical lattice generating adversarial network (SLGAN) for the remote sensing images (RSIs) compression in this paper. Several pairs of symmetrical encoder-decoder lattices are constructed to build a generator to first generate deep representative codes of images and then decode them. For each pair of encoded lattice and decoded lattice, one discriminator is constructed to perform adversarial learning with the generator. When multiple discriminators are used for all the lattices, a cooperative learning algorithm is proposed to train jointly pairs of symmetric lattices in the generator. Moreover, to enhance edges, contours, and textures in the decomposed RSIs, an enhanced Laplacian of gaussian (ELoG) loss is designed as a regularizer to train the SLGAN. Experimental results on the panchromatic images from GF2 satellite show that SLGAN outperforms other existing state-of-the-art methods.

图像压缩通常包括两个重要的操作：压缩和解压缩。压缩过程包括丢弃信息的操作，而解压缩过程则是检索丢失的信息。为了使解压缩后的图像更类似于原始图像，经典的压缩方法通常采用近似可逆的压缩和解压缩过程。受到经典压缩方法中对称结构的启发，本文提出了一种新的对称格网生成对抗网络（SLGAN），用于遥感图像（RSIs）压缩。构建几对对称的编码器/解码器晶格以构建生成器，以首先生成图像的深层代表性代码，然后对其进行解码。对于每对编码格和解码格，构造了一个鉴别器以与生成器进行对抗性学习。当对所有晶格使用多个鉴别器时，提出了一种协同学习算法来共同训练生成器中的对称晶格对。此外，为了增强分解后的RSI中的边缘，轮廓和纹理，设计了增强的高斯Laplacian高斯（ELoG）损耗作为正则化器来训练SLGAN。对来自GF2卫星的全色图像的实验结果表明，SLGAN优于其他现有的最新方法。和分解后的RSI中的纹理，高斯高斯（ELoG）损失的拉普拉斯算子被设计为训练SLGAN的正则化器。对来自GF2卫星的全色图像的实验结果表明，SLGAN优于其他现有的最新方法。和分解后的RSI中的纹理，高斯高斯（ELoG）损失的拉普拉斯算子被设计为训练SLGAN的正则化器。对来自GF2卫星的全色图像的实验结果表明，SLGAN优于其他现有的最新方法。