粒子群空间优化的端元提取算法  

Endmember extraction algorithm based on particle swarm search space optimization

作　　者：祝章智 黄风华[1] ZHU Zhangzhi;HUANG Fenghua(Provincial Spatial Information Engineering Research Center, Fuzhou University, Fuzhou 350002, China;Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China;National Engineering Research Centre of Geospatial Space Information Technology, Fuzhou University, Fuzhou 350002, China)

机构地区：[1]福州大学福建省空间信息工程研究中心,福州350002 [2]福州大学空间数据挖掘与信息共享教育部重点实验室,福州350002 [3]福州大学地理空间信息技术国家地方联合工程研究中心,福州350002

出　　处：《计算机工程与应用》2018年第11期185-192,共8页Computer Engineering and Applications

基　　金：国家自然科学基金(No.41501451)

摘　　要：粒子群优化算法(Particle Swarm Optimization,PSO)应用于高光谱影像端元提取时,由于影像中存在端元的像元数所占比例极小且分布零散,导致粒子群的搜索空间破碎,存在收敛性能低、容易陷入局部最优解等缺陷。对粒子群的搜索空间进行优化,选择影像中纯净像元指数(Pixel Purity Index,PPI)较大的像元作为预选像元,然后对预选像元进行光谱聚类排序,将排序后的集合作为粒子群的搜索空间,优化了粒子的搜索空间。并在迭代过程中,充分利用粒子群的信息自适应地调整其系数,在缩小原始图像与反演图像的误差同时,增加体积约束,在提取端元时更好地保持其原有的形状。通过模拟数据和AVIRIS影像的实验表明该算法具有较好端元提取效果。Particle Swarm Optimization（PSO）is an optimization algorithm based on continuous space. Because the number of endmembers is small and endmembers are scattered in the hyperspectral image, the search space of the PSO is scattered.The traditional PSO algorithm has the weaknesses of being sensitive to initial value, low convergency, easy to fall into the local optimum. To solve this problem, this paper selects the pixels with high Pure Pixel Index（PPI）as the preselected pixels, and sorts the preselected pixels. Finally, the sorting pixels are taken as the searching space of PSO, to reduce the search space and improve the efficiency of the algorithm. Experimental results show that this algorithm has better result than other algorithms in simulating and AVIRIS images.

关 键 词：粒子群算法 端元提取 高光谱遥感 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]