Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art

The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible, helping to move from a two-dimensional (2D) or three-dimensional (3D) data representation of four-dimensional (4D) data structures, where the time variable adds new information – and challenges – for information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded according to the different paths taken by each research community. This article brings together the advances of multisource and multitemporal data fusion approaches with respect to the various research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references. More specifically, this work provides a bird’s-eye view of many important contributions specifically dedicated to the topics of pansharpening and resolution enhancement, point cloud data fusion, hyperspectral and lidar data fusion, multitemporal data fusion, and big data and social media. In addition, the main challenges and possible future research in each area are outlined and discussed.



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