SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain

Abstract

The synthesis of spectral remote sensing images of the Earth’s background is affected by various factors such as the atmosphere, illumination and terrain, which makes it difficult to simulate random disturbance and real textures. Based on the shared latent domain hypothesis and generation adversarial network, this paper proposes the SDTGAN method to mine the correlation between the spectrum and directly generate target spectral remote sensing images of the Earth’s background according to the source spectral images. The introduction of shared latent domain allows multi-spectral domains connect to each other without the need to build a one-to-one model. Meanwhile, additional feature maps are introduced to fill in the lack of information in the spectrum and improve the geographic accuracy. Through supervised training with a paired dataset, cycle consistency loss, and perceptual loss, the uniqueness of the output result is guaranteed. Finally, the experiments on the Fengyun satellite observation data show that the proposed SDTGAN method performs better than the baseline models in remote sensing image spectrum translation.

Artificial Intelligence-Based Learning Approaches for Remote Sensing

“…Remote sensing data of the Earth are influenced by numerous factors. Based on common/known hypotheses and a generation adversarial network, the contribution by Wang et al [13] presented the SDTGAN approach to connect spectral data and to directly create target spectral remote sensing data. Additionally, more feature map information is presented to compensate for the lack of information in the spectral data and to enhance the geographic precision.…”

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

“…Various deep learning networks have been proposed to improve reconstruction accuracy, from simple network models to more advanced deep learning networks using a variety of techniques. These data-driven methods, which primarily use supervised learning, include CNN-based methods [ 14 , 27 ], GAN-based methods [ 16 , 28 , 29 ] and Attention-based methods [ 18 , 30 , 31 ]. CNN-based methods have strong nonlinear characterization capability and are widely used in SR.…”