| 一种顾及上下文的高光谱遥感图像端元提取方法 |
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| 引用本文： | 杜会建,赵银娣,蔡燕.一种顾及上下文的高光谱遥感图像端元提取方法[J].测绘科学,2012,37(2):126-128,32. |
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| 作者姓名： | 杜会建 赵银娣 蔡燕 |
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| 作者单位： | 1. 中国矿业大学国土环境与灾害监测国家测绘局重点实验室,江苏徐州221116;中国矿业大学环境与测绘学院,江苏徐州221116
2. 中国矿业大学环境与测绘学院,江苏徐州221116;中国矿业大学江苏省资源环境信息工程重点实验室,江苏徐州221116 |
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| 摘 要： | 端元提取技术是混合像元分解中重要的步骤之一,传统的端元提取方法仅考虑了像元的光谱信息。本文将数学形态学算子扩展到高光谱空间,并应用到端元提取技术中,可以顾及像元的上下文信息。利用AVIRIS高光谱仿真数据对算法进行了实验验证,结果表明本文算法具有较强的抗噪能力和较高的可靠性。在此基础上,结合徐州地区的EO-1 Hyperion高光谱遥感图像,使用本文算法进行了端元提取应用研究,将实验结果与纯净像元指数、顶点成分分析方法做了对比分析和精度评价,证明本文算法是一种可靠的高光谱遥感图像端元提取技术。
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| 关 键 词： | 端元提取 数学形态学 上下文信息 高光谱遥感 |
Contextual endmember extraction of hyperspectral remote sensing image |
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| DU Hui-jian
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ZHAO Yin-di
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CAI Yan.Contextual endmember extraction of hyperspectral remote sensing image[J].Science of Surveying and Mapping,2012,37(2):126-128,32. |
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| Authors: | DU Hui-jian ZHAO Yin-di CAI Yan |
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| Institution: | ②③(China University of Mining and Technology,Key Laboratory for Land Environment and Disaster Monitoring of SBSM,Jiangsu Xuzhou 221116,China;②School of Environment Science and Spatial Informatics,China University of Mining and Technology,Jiangsu Xuzhou 221116,China;③China University of Mining and Technology,Jiangsu Key Laboratory of Resources and Environmental Information Engineering,Jiangsu Xuzhou 221116,China) |
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| Abstract: | Endmember extraction technology is a significant part of Spectral Mixture Analysis,the traditional endmember extraction algorithms only use spectral information.The proposed algorithm based on context could use both spectral and spatial information.AVIRIS simulated data was used to verify this algorithm’s performance,and the results showed that the proposed algorithm has strong antinoise ability and high reliability.On this basis,taking EO-1 Hyperion image of Xuzhou city in 2006 as experimental data,three algorithms(the proposed algorithm,Pixel Purity Index,Vertex Component Analysis) were used for endmember extraction.The results of comparison indicated that the proposed algorithm could be a reliable endmember extraction technology.Finally,abundance maps were obtained based on Back-Propagation Neural Network. |
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| Keywords: | endmember extraction mathematical morphology context information hyperspectral remote sensing |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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