Data fusion is an important means of improving the applicability of remote sensing images

and has long been a hot research topic in the remote sensing field. This paper reviews the progress and future of remote sensing data fusion. First

the hierarchy and category of data fusion are summarized

and remote sensing data fusion methods are classified into four categories

namely

homogeneous data fusion

heterogeneous data fusion

fusion for remote sensing observation and station data

and fusion for remote sensing observation and non-observed data. Second

this paper discusses spatio-temporal-spectral fusion of optical remote sensing data

including multi-view spatial fusion

multi-scale fusion

spatio-spectral fusion

spatio-temporal fusion

and integrated spatio-temporal-spectral fusion. Third

this paper discusses the prospective direction of remote sensing data fusion literature

including the extension of integrated spatio-temporal-spectral fusion

across-scale fusion from aerospace to ground observations

online fusion in sensor web environment

and application-oriented fusion.