首页|期刊简介|投稿指南|分类索引|刊文选读|订阅指南|资料|样刊邮寄查询|常见问题解答|联系我们
杨华东,许 楠. 基于数据约减和中心化的像元纯净指数端元提取方法[J]. 科学技术与工程, 2020, 20(23): 9503-9508.
YANG Hua-dong,XU Nan.Pixel Purity Index Endmember Extraction Algorithm Based on Data Reduction and Centralization[J].Science Technology and Engineering,2020,20(23):9503-9508.
基于数据约减和中心化的像元纯净指数端元提取方法
Pixel Purity Index Endmember Extraction Algorithm Based on Data Reduction and Centralization
投稿时间:2019-10-30  修订日期:2020-05-08
DOI:
中文关键词:  端元提取 像元纯净指数 正交投影 数据中心化 数据约减
英文关键词:endmember extraction  pixel purity index  orthogonal projection  data centralization  data reduction
基金项目:辽宁省自然科学基金指导计划项目
     
作者单位
杨华东 沈阳理工大学
许 楠 沈阳建筑大学
摘要点击次数: 97
全文下载次数: 39
中文摘要:
      像元纯净指数(Pixel Purity Index, PPI)算法是最为常用的端元提取算法之一,但算法中投影向量的随机性导致多次运行的端元提取结果不一致。为此,提出一种基于数据约减和中心化的像元纯净指数端元提取方法(Pixel Purity Index Endmember Extraction Algorithm Based on Data Reduction and Centralization, DRC-PPI)。DRC-PPI首先利用自动目标生成算法生成候选端元,并进行无约束最小二乘解混,将解混丰度为负的像元从原始数据中移除得到约减数据。其次,对约减数据进行数据中心化进而获得投影向量,将约减数据投影到这些向量上,然后根据样本点的像元纯净指数选择端元光谱。仿真数据和真实高光谱数据实验结果表明,DRC-PPI算法克服了PPI端元提取结果不一致性,大大减少了投影计算量,其端元提取精度总体上高于PPI算法。
英文摘要:
      Pixel purity index is one of widely used endmember extraction algorithm, which identifies endmembers by projecting data sample vectors on a set of projection vectors, however, these vectors must be generated in a random manner which results in the final extracted endmembers are inconsistent and non-reproducible. This study derives a novel endmember extraction algorithm to resolve this issue. It firstly generates endmember candidates and estimates unconstrained abundance using automatic target generate process algorithm and least square estimation method, respectively. And then, it obtains reduction data by removing the pixel with negative abundance from spectral data. The projection vectors can be obtained directly by centralizing and unitizing the reduction data vector. Finally, the reduction data vectors are orthogonally projected onto the projection vectors, and endmember can be identified in terms of pixel purity index of each pixel. Both synthetic and real hyperspectral image experimental results demonstrate that the proposed algorithm could overcome the inconsistency and non-reproducibility caused by random projection vectors, and could reduce projection computation in much degree.
查看全文  查看/发表评论  下载PDF阅读器
关闭
你是第32690661位访问者
版权所有:科学技术与工程编辑部
主管:中国科学技术协会    主办:中国技术经济学会
Tel:(010)62118920 E-mail:stae@vip.163.com
京ICP备05035734号-4
技术支持:本系统由北京勤云科技发展有限公司设计

京公网安备 11010802029091号