| 结合空间信息选取最优端元组合的混合像元分解 |
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| 引用本文： | 徐君,王彩玲.结合空间信息选取最优端元组合的混合像元分解[J].测绘通报,2019,0(7):6-11. |
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| 作者姓名： | 徐君 王彩玲 |
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| 作者单位： | 西安航空学院电子工程学院,陕西西安,710077;西安石油大学计算机学院,陕西西安,710065 |
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| 基金项目： | 国家自然科学基金（61763010）；西安航空学院校立科研项目（2018KY0209）；陕西省重点研发计划（2019GY-112） |
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| 摘 要： | 传统的混合像元分解算法认为每个像元都包含图像中所能提取的全部端元组分，但这并不符合实际情况。实际上图像中大多数混合像元仅由少部分端元混合而成。由于端元提取精度及噪声的影响，采用全部端元对混合像元进行分解，会使得混合像元中实际并不存在的端元的丰度估计值不为零，分解结果存在较大误差。由于混合像元大多存在于不同地物的交界处，基于此，本文提出了一种结合图像的空间信息选取混合像元最优端元子集的方法。利用一个空间结构元素，从混合像元的附近邻域开始搜索，将搜索到的纯净像元光谱与所提取的图像端元光谱进行对比，并确定混合像元的端元子集进行分解。根据RMSE大小和变化情况，逐步扩大结构元素的大小，不断调整搜索范围，直至得到最优端元组合。模拟数据和真实数据的试验结果表明，该方法相比传统的全端元光谱分解方法，在总体上获得了更好的分解效果。
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| 关 键 词： | 高光谱图像 混合像元 端元可变 |
| 收稿时间： | 2018-07-02 |
A spectral unmixing method of using spatial information to select optimal endmember subset |
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| XU Jun,WANG Cailing.A spectral unmixing method of using spatial information to select optimal endmember subset[J].Bulletin of Surveying and Mapping,2019,0(7):6-11. |
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| Authors: | XU Jun WANG Cailing |
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| Institution: | 1. School of Electronic Engineering, Xi'an Aeronautical University, Xi'an 710077, China;2. School of computer, Xi'an Shiyou University, Xi'an 710065, China |
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| Abstract: | The traditional spectral unmixing algorithm considers that each pixel contains all the endmembers extracted from the image, which does not conform to the actual situation. In fact, most of the mixed pixels in the image are only mixed by a small number of endmembers. Because of the influences of endmember extraction precision and noise, if all endmembers are used in spectral unmixing, it will make the abundances of the endmembers which are not involved in the mixed pixel are not zero, the spectral unmixing results have large errors. Because most of the mixed pixels are located at the junction of different ground objects, this paper proposes a method to select the optimal endmember subset of mixed pixels by utilizing the spatial information of the image. Using a spatial structure element, this method starts to search pure pixel spectrum from the adjacent domains of the mixed pixels, then compares the searched pure pixel spectrum with the previously extracted image endmembers to determine the endmember subset of the mixed pixels. According to the variation of RMSE, the size of the structural element is gradually expanded, and the search scope is constantly adjusted until the optimal endmember set is obtained. The experimental results of the simulated data and the real data show that the proposed method has a relatively better spectral unmixing effect compared with the traditional spectral unmixing method using all endmembers. |
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| Keywords: | hyperspectral image mixed pixel selective endmember |
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