Symmetrical lattice generative adversarial network for remote sensing images compression

Abstract

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.