Mathematical Methods in High Performance Computing Hyperspectral Imaging

Dear Colleagues,

Hyperspectral imaging has been established as one of the most common and useful technologies used for remote sensing. It is a technique that generates hundreds of images, corresponding to different wavelength channels (bands) for the same area on the surface of the Earth. Mathematical methods used for hyperspectral data processing have a very high computational complexity: any generalization of mathematical methods for two-dimensional images has an overhead, at least, of the order of the number of bands. High-performance computing has been used to map hyperspectral image analysis mathematical methods in many remote sensing applications, including environmental modeling, biological threat detection, the monitoring of oil spills, target detection for military and defense/security purposes, and wildfire tracking.

We invited our colleagues to submit papers related to mathematical methods of high-performance computing hyperspectral imaging. These include, but are not limited to, new mathematical methods for hyperspectral image analysis; the modification of well-known mathematical methods for hyperspectral image analysis to reduce its computational complexity; the conversion of hyperspectral image analysis mathematical methods from the floating point to fixed point or to a new numerical representation format; and the implementation of mathematical methods for hyperspectral image analysis in high-performance computing technologies, such as multicore, graphics processing units, field-programmable gate array, or heterogeneous platforms.

Prof. Dr. Carlos González Calvo
Guest Editor

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• hyperspectral images
• mathematical methods
• high-performance computing
• multicore
• graphics processing units
• field-programmable gate array