NASA Technical Reports Server (Ntrs) 20150021840: Radiation Tolerant, Fpga-Based Smallsat Computer System by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 20150021840: Radiation Tolerant, Fpga-Based Smallsat Computer System by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 20150013989: Real-Time on-Board Airborne Demonstration of High-Speed on-Board dataprocessing for Science Instruments (Hops) by NASA Technical Reports Server (Ntrs); NASA Technical...
详细信息
NASA Technical Reports Server (Ntrs) 20150013989: Real-Time on-Board Airborne Demonstration of High-Speed on-Board dataprocessing for Science Instruments (Hops) by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19980227015: New Opportunities with the Advanced Communications Technology Satellite (Acts) by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19980227015: New Opportunities with the Advanced Communications Technology Satellite (Acts) by NASA Technical Reports Server (Ntrs); published by
Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating larg...
详细信息
ISBN:
(纸本)9781479985692
Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.
暂无评论