Endmember Extraction from Hyperspectral Images Based on Genetic Algorithm and Volume Maximization
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维普
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针对目前体积最大化端元提取技术存在提取精度低、计算复杂性高等问题,提出了结合遗传算法和体积最大化的端元提取方法(Extraction Method Based on Genetic Algorithm and Volume Maximization,EE-GAVM),以改进现有的端元提取技术.EE-GAVM方法通过寻找单纯形体积最大的像元向量集合来定位到端元集合,将端元提取问题表述为单一目标,节约计算资源的同时实现了精度的提高.实验结果表明:所提方法在性能和精度方面优于其他对比算法.
Aiming at the problems of low extraction accuracy and high computational complexity in the current volume maximization endmember extraction technology,the Extraction Method Based on Genetic Algorithm and Volume Maximization (EE-GAVM) is proposed to improve the existing endmember extraction methods. The EE-GAVM method locates the endmember set by searching for the pixel vector set with the largest volume of the simplex,and expresses the endmember extraction problem as a single target,which saves computing resources and improves accuracy. The experimental results show that the proposed method outperforms other comparative algorithms in terms of performance and accuracy.
维普
万方数据
