Abstract:

Recent advances in multispectral and hyperspectral sensing technologies coupled with rapid growth in computing power have led to new opportunities in remote sensing—higher spatial and/or spectral resolution over larger areas leads to more detailed and comprehensive land cover mapping and more sensitive target detection. However, these massive hyperspectral datasets provide new challenges as well. Accurate and timely processing of hyperspectral data in large volumes must be treated in a nonconventional way in order to drastically enhance data modeling and representation, learning and inference, physics-based analysis, computational complexity, and so forth. Current practical issues in processing multispectral and hyperspectral data include robust characterization of target and background signatures and scene characterization, joint exploitation of spatial and spectral features, background modeling for anomaly detection, robust target detection techniques, low-dimensional representation, fusion of learning algorithms, the balance of statistical and physical modeling, and real-time computation

Links:

Citation:

H. Kwon, X. Hu, J. Theiler, A. Zare, and P. Gurram, “Editorial: Algorithms for multispectral and hyperspectral image analysis,” J. Electr. Computer Eng., vol. 2013, 2013.

@Article{kwon2013algorithms,
Title = {Editorial: Algorithms for multispectral and hyperspectral image analysis},
Author = {Kwon, Heesung and Hu, Xiaofei and Theiler, James and Zare, Alina and Gurram, Prudhvi},
Journal = {J. Electr. Computer Eng.},
Year = {2013},
Volume = {2013},
Doi = {10.1155/2013/908906},
}