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In that line, this paper describes the construction of a new hyperspectral processing library for RVC–CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This paper presents the development of the required library functions to implement two of the four stages of the hyperspectral imaging processing chain--endmember and abundances estimation. The results obtained show that the library achieves speedups of 30%, approximately, comparing to an existing software of hyperspectral images analysis; concretely, the endmember estimation step reaches an average speedup of 27.6%, which saves almost 8 seconds in the execution time. It also shows the existence of some bottlenecks, as the communication interfaces among the different actors due to the volume of data to transfer. Finally, it is shown that the library considerably simplifies the implementation process. Thus, experimental results show the potential of a RVC–CAL library for analyzing hyperspectral images in real-time, as it provides enough resources to study the system performance. |
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Hyperspectral imaging
Image processing
Algorithm development
Image analysis
Signal to noise ratio
Cancer
Matrices
