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Accepted author manuscript, 5.52 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 1/03/2019 |
|---|---|
| <mark>Journal</mark> | IEEE Geoscience and Remote Sensing Magazine |
| Issue number | 1 |
| Volume | 7 |
| Number of pages | 34 |
| Pages (from-to) | 6-39 |
| Publication Status | Published |
| <mark>Original language</mark> | English |
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.