Publications

Song, YY; Zhang, HY; Huang, H; Zhang, LP (2022). Remote Sensing Image Spatiotemporal Fusion via a Generative Adversarial Network With One Prior Image Pair. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 60, 5528117.

Abstract
Spatiotemporal fusion (STF) is an effective solution to promote the application of remote sensing images, given that the tradeoff between the temporal resolution and the spatial resolution is ubiquitous in the production of remote sensing images. However, cloud coverage makes it difficult to obtain dense cloud-free Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) image pairs on the timeline, which limits the application of existing STF methods. Considering the lack of prior image pairs and the huge spatial resolution gap between Landsat and MODIS images, this article presents a novel remote sensing image STF method based on a generative adversarial network to handle one Landsat-MODIS prior image pair case (OPGAN), which contains a generator and a discriminator simultaneously trained in a min-max game. OPGAN is built based on the STF observation model that learns the base information from the prior Landsat image and then captures temporal change (TC) information from a difference image constructed from MODIS images collected at times 1 and 2 and sensor difference information from the difference image between Landsat and MODIS images at time 1. They are combined together to reconstruct the Landsat image at time 2 at both high spatial and high temporal resolution. Moreover, a change loss is proposed to further improve the accuracy of TC prediction. Extensive experiments on the STF dataset illustrate that the proposed OPGAN method can obtain more accurate prediction of spatial information and TCs in the case of insufficient prior information.

DOI:
10.1109/TGRS.2022.3171331

ISSN:
1558-0644