| 高分辨率图像辅助提取高光谱图像端元 |
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| 引用本文： | 崔宾阁,张杰,马毅,任广波.高分辨率图像辅助提取高光谱图像端元[J].遥感学报,2014,18(1):192-205. |
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| 作者姓名： | 崔宾阁 张杰 马毅 任广波 |
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| 作者单位： | 山东科技大学 信息科学与工程学院, 山东 青岛 266590;国家海洋局 第一海洋研究所, 山东 青岛 266061;国家海洋局 第一海洋研究所, 山东 青岛 266061;国家海洋局 第一海洋研究所, 山东 青岛 266061;国家海洋局 第一海洋研究所, 山东 青岛 266061 |
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| 基金项目： | 国家自然科学基金（编号：41206172） |
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| 摘 要： | 现有的端元提取算法大多是基于凸面单形体假设,对于非单一地物类型,利用这些端元进行丰度反演将会影响混合像元分解精度。本文提出一种利用高分辨率图像判断高光谱像元内是否为同一类型地物的方法。首先,利用图像分割程序对高分辨率图像进行分割,得到光谱均一的斑块矢量图,并叠加到高光谱图像上;然后,通过空间关系分析找出斑块内的高光谱像元,称其为准端元;最后,利用端元提取算法在这些准端元中进行端元提取。实验结果表明,该方法将端元提取结果的误差降低了20%左右。
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| 关 键 词： | 端元提取 高分辨率 高光谱 分割 准端元 |
| 收稿时间： | 2013/3/28 0:00:00 |
| 修稿时间： | 2013/7/19 0:00:00 |
High-resolution image-assisted endmember extraction of hyperspectral image |
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| CUI Bin''ge,ZHANG Jie,MA Yi and REN Guangbo.High-resolution image-assisted endmember extraction of hyperspectral image[J].Journal of Remote Sensing,2014,18(1):192-205. |
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| Authors: | CUI Bin'ge ZHANG Jie MA Yi and REN Guangbo |
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| Institution: | College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China |
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| Abstract: | Existing endmember extraction algorithms are mainly based on the convex simplex hypothesis. However, the cover types in certain endmembers are not single, which will affect the unmixing accuracy of mixed pixels when performing abundance inversion. In this paper, we propose to determine the nature of the hyperspectral pixel based on the high-resolution remote sensing image. First, a spectral relatively homogeneous vector diagram of blocks is superimposed on the hyperspectral image after the high-resolution image segmentation. Second, spatial relations analysis is performed to find the hyperspectral pixels that are within the blocks, which is called a quasi-endmember. Finally, endmember extraction is performed to find endmembers from the quasi-endmember set. The experimental results demonstrate that our approach can reduce the root mean square error of the extraction results by about 20%. |
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| Keywords: | endmember extraction high-resolution hyperspectral segmentation quasi-endmember |
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