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

By Pedram Ghamisi, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo and 2 more. IEEE Geoscience and Remote Sensing Magazine, 2019

393 citations recorded by the source corpus.

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

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/73939a83721b4263ce4d697308431c6c4271ad22

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Environmental Engineering

In Media Technology

Citations in the corpus

Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors (2019, background): “classify both our early and late fusion schemes as complementary, when the sensors provide exclusive ﬁeld-of-views, and as redundant, when using sensors with overlapping ﬁeld-of-views. Ghamisi et al. [26] reviews three categories of point cloud fusion for remote sensing: point cloud level, where mor”
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs (2020, methodology): “constituents, X h often exhibits nonlinear relationships between the captured spectral information and the corresponding material. This nonlinear characteristic will be magniﬁed when dealing with X l [7]. It has been proved that CNNs are capable of extracting high-level features, which are usually i”
Multimodal Fusion Transformer for Remote Sensing Image Classification (2022, background): “8, 9, 10, 11, 12, and 13 depict the obtained classiﬁcation maps for the hyperspectral datasets i.e., Houston, Trento, MUUFL and Augsburg along with their corresponding other modalities like LiDAR, MS image, SAR and DSM data, respectively.”
UAV & satellite synergies for optical remote sensing applications: A literature review (2021, neutral): “Indeed, this exponential production of EO data since the beginning of the 2000s can be partly explained by the increase in the number of satellites in orbit (Ghamisi et al., 2019) and the democratization of UAVs due to the decreasing cost of exploitation (Sun and Scanlon, 2019).”