Liu, X., Jiao, L., Zhao, J., Zhao, J., Zhang, D., Liu, F., et al. (2018) Deep Multiple Instance Learning-Based Spatial-Spectral Classification for PAN and MS Imagery. IEEE Transactions on Geoscience and Remote Sensing, 56, 461-473.
https://doi.org/10.1109/TGRS.2017.2750220

has been cited by the following article:

• TITLE: Multi-Sensor Image Fusion: A Survey of the State of the Art

  AUTHORS: Bing Li, Yong Xian, Daqiao Zhang, Juan Su, Xiaoxiang Hu, Weilin Guo

  KEYWORDS: Multi-Sensor Image Fusion, Fusion Strategy, Feature Enhancement, Fusion Performance Assessment

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.6, June 21, 2021

  ABSTRACT: Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.