Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100094, China.
China Academy of Space Technology (CAST), Beijing 100081, China.
Sensors (Basel). 2019 Jan 31;19(3):598. doi: 10.3390/s19030598.
Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods.
光谱分解是高光谱遥感图像应用中的一个重要过程。线性混合模型已被广泛用于通过提取一组称为高光谱术语中的端元的纯光谱特征，并估计它们在场景中每个像素的相应分数丰度，来对高光谱图像进行分解。已经提出了许多自动提取端元的算法，这是光谱分解链中的关键步骤。近年来，蚁群优化（ACO）算法已被开发用于从高光谱数据中提取端元，这被视为组合优化问题。尽管用于端元提取的蚁群优化（ACOEE）可以获得准确的端元结果，但它的高计算复杂度限制了它在高光谱数据分析中的应用。GPU 并行计算技术可用于提高 ACOEE 的计算性能，但 GPU 的架构决定了 ACOEE 应该重新设计，以充分利用 GPU 上的计算资源。在本文中，提出了一种基于多个子蚁群的 ACOEE 并行设计，其中利用了子蚁群的局部信息素的创新机制，使 ACOEE 能够更好地在多 GPU 系统上执行。所提出的方法可以避免不同 GPU 之间的大量同步，从而影响计算性能的提高。在两个真实高光谱数据集上的实验表明，ACOEE 的计算性能从所提出的方法中显著受益。
