Parallel morphological endmember extraction using commodity graphics hardware

Abstract—Spatial/spectral algorithms have been shown in pre-vious work to be a promising approach to the problem of extract-ing image endmembers from remotely sensed hyperspectral data. Such algorithms map nicely on high-performance systems such as massively parallel clusters and networks of computers. Unfortu-nately, these systems are generally expensive and difficult to adapt to onboard data processing scenarios, in which low-weight and low-power integrated components are highly desirable to reduce mission payload. An exciting new development in this context is the emergence of graphics processing units (GPUs), which can now satisfy extremely high computational requirements at low cost. In this letter, we propose a GPU-based implementation of the automated morphological endmember extraction algorithm, which is used in this letter as a representative case study of joint spatial/spectral techniques for hyperspectral image processing. The proposed implementation is quantitatively assessed in terms of both endmember extraction accuracy and parallel efficiency, using two generations of commercial GPUs from NVidia. Com-bined, these parts offer a thoughtful perspective on the potential and emerging challenges of implementing hyperspectral imaging algorithms on commodity graphics hardware. Index Terms—Commodity graphics hardware, endmember extraction, spatial/spectral analysis. I