REMOTE SENSING IMAGE SPATIO-TEMPORAL FUSION VIA A GENERATIVE ADVERSARIAL NETWORK THROUGH ONE PRIOR IMAGE PAIR

Spatio-temporal fusion is a promising way to deal with the tradeoff between the temporal resolution and spatial resolution of the remote sensing images This paper presents a novel remote sensing image spatio-temporal fusion model to expand the application of spatio-temporal fusion with insufficient data, based on a generative adversarial network to handle one prior image pair cases (OPGAN). Considering the huge spatial resolution gap between the high-spatial, low-temporal (HSLT) resolution Landsat imagery and the corresponding low-spatial, high-temporal (LSHT) resolution MODIS imagery, the proposed OPGAN simultaneously trains a generator and a discriminator in a min-max game to reconstruct the high-spatial-high-temporal (HSHT) resolution Landsat images, significantly improving the accuracy of change prediction with the help of the temporal changes and sensor differences. Experimental results on three representative Landsat-MODIS datasets illustrate the effectiveness of the proposed OPGAN method.