Progress and future of remote sensing data fusion

Abstract

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.