Paolo Gamba - Publications#


Number of cited papers378457
Total number of citations73349602

10 MOST CITED AND/OR MOST RECENT PUBLICATIONS (in decreasing order of the number of citations]

[1] A. Plaza, J. Benediktsson, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, G. Trianni, “Recent advances in techniques for hyperspectral image processing”, Remote Sensing of the Environment, vol. 113, Supplement 1, pp. S110-S122, Sept. 2009. [1336 citations]]

A review paper introducing benchmarking hyperspectral image processing techniques after the establishment of the HYSENS project by A. Plaza and P. Gamba.

[2] L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L. Mann Bruce, “Comparison of Pansharpening Algorithms, Outcome of the 2006 GRS-S Data Fusion Contest”, IEEE Trans. Geoscience and Remote Sensing, vol. 45, no. 10, pp. 3012-3021, Oct. 2007. [686 citations]]

The paper summarizing the results of the first Data Fusion Contest, organized by P. Gamba in 2006, the first of a series of very successful international contests by the IEEE Data Analysis Technical Committee.

[3] J. Li, X. Huang, P. Gamba, J.M. Bioucas Dias, L. Zhang, J.A. Benediktsson, A. Plaza, “Multiple Feature Learning for Hyperspectral Image Classification”, IEEE Trans. Geoscience and Remote Sensing, doi: 10.1109/TGRS.2014.2345739, vol. 53, no. 3, pp. 1592–1606, 2015. [284 citations]]

A seminal paper introducing the concept of multiple feature learning as a result of the joint research work by P. Gamba and the most important researchers in hyperspectral data processing.

[4] N.N. Patel, E. Angiuli, P. Gamba, A. Gaughan, G. Lisini, F.R. Stevens, A.J. Tatem, and G. Trianni “Multitemporal settlement and population mapping from Landsat using Google Earth Engine”, J. of Applied Earth Observation and Remote Sensing, doi: 10.1016/j.jag.2014.09.005, Vol. 35, Part B, pp. 199-208, March 2015. [208 citations]]

A paper in the most important IEEE journal in the area of remote sensing describing the results of a project funded by Google and lead by P. Gamba.

[5] Z.Li, H. Shen, H. Li, G. Xia, P. Gamba, L. Zhang, “Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery“, Remote Sensing of Environment, doi: 10.1016/j.rse.2017.01.026, vol. 191, pp. 342–358, 2017. [162 citations]]

A recent paper with describing a smart approach for cloud screening in optical images published in the journal with the largest impact factor in the area of remote sensing.

[[[6] M. Dalla Mura, F. Pacifici, S. Prasad, P. Gamba, J. Chanussot, “Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing”, Proc. of IEEE, doi: 10.1109/JPROC.2015.2462751, vol. 103, no. 9, pp. 1585-1601, 2015. [140 citations]]

The first review paper about multimodal data fusion, i.e., the fusion of multiple heterogeneous sensors, for remote sensing applications. This work, published in the flagship journal of IEEE, the Proceedings of IEEE, paved the way to multimodal joint remote sensing data analysis.

[7] Y. Su, J. Li, A. Plaza, A. Marinoni, P. Gamba, and S. Chakravortty, “DAEN: Deep Auto-Encoder Networks for Hyperspectral Unmixing”, IEEE Trans. on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2018.2890633, Vol. 57, n.7, pp. 4309-4321, July 2019. [130 citations]]

A recent paper introducing one of the first examples of hyperspectral unmixing by deep learning approaches and published in the most importat IEEE journal in the area of remote sensing.

[8] Y. Ban, A. Jacob, and P. Gamba, “Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Using a Robust Urban Extractor”, ISPRS J. of Photogrammetry and Remote Sensing, doi: 10.1016/j.isprsjprs.2014.08.004, vol. 103, pp. 28-37, 2015. [87 citations]]

A recent paper highliting an efficient approach for urban area extent extraction from SAR images, the result of a project funded by ESA in which P. Gamba lead the research unit of Pavia.

[9] L. Gao, D. Hong, Member, J. Yao, B, Zhang, P. Gamba, and J. Chanussot, “Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning”, IEEE Trans. Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3000684, vol. 59, no. 3, pp. 2269-2280, March 2021. [68 citations]]

A very recent paper on superresolution of multispecral (and, in perspective, hyperspectral) data sets, a research area in which P. Gmaba has been working for years.

[10] Y. Su, A. Marinoni, J. Li, J. Plaza, P. Gamba, “Stacked Nonnegative Sparse Auto-encoders for hyperspectral Unmixing”, IEEE Geoscience and Remote Sens. Lett., doi: 10.1109/LGRS.2018.2841400, vol. 15, no. 9, pp. 1427-1431, Sept. 2018. [60 citations]]

A letter paper meant to provide for the first time a working Deep Learning algorithm for hyperspectral data classification.

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