| 基于混合像元分解与EM算法的中低分辨率遥感影像变化检测 |
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| 引用本文： | 吴柯,何坦,杨叶涛.基于混合像元分解与EM算法的中低分辨率遥感影像变化检测[J].武汉大学学报(信息科学版),2019,44(4):555-562. |
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| 作者姓名： | 吴柯 何坦 杨叶涛 |
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| 作者单位： | 1.中国地质大学地球物理与空间信息学院, 湖北 武汉, 430074 |
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| 基金项目： | 国家自然科学基金61372153中央高校基本科研业务费专项CUGL140410中央高校基本科研业务费专项26420160125 |
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| 摘 要： | 中低分辨率遥感影像中广泛存在的混合像元极大地限制了变化检测结果的精度。基于混合像元分解技术，能够深入到像元内部，比较不同端元的组成分差异影像，然后获取亚像元级别的变化信息。如何从差异影像中确定合适的变化阈值，从而准确地判断变化是否发生，是一个难点问题。在高斯模型分布假设的情况下，采用最大期望法（expectation maximization，EM）自动提取最佳阈值，完成自适应的变化检测过程。选择了两种典型的阈值选择方法与该方法进行比较，结果证明基于EM的自适应变化检测方法可以更准确地提取变化信息，具有较好的稳健性。
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| 关 键 词： | 变化检测 混合像元分解 端元 EM 差异影像 |
| 收稿时间： | 2017-09-18 |
Change Detection Method Based on Pixel Unmixing and EM Algorithm for Low and Medium Resolution Remote Sensing Imagery |
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| Affiliation: | 1.Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China2.Changjiang River Sand-Mining Management Bureau of Changjiang Water Resources Commission, Wuhan 430012, China |
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| Abstract: | The low spatial resolution images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection. Pixel unmixing can produce the proportional fractions of land-covers, on which sub-pixel information can be extracted to reduce the low-spatial-resolution-problem to some extent. Therefore, the sub-pixel change detection can be proposed to overcome this issue in this paper. Firstly, the endmembers of different temporal remote sensing images are extracted; after that, the pixel unmixing process is implemented and the abundance difference image is produced through comparison with the fractional abundances. The endmember variability per pixel is considered during the process to ensure the high accuracy of derived proportional fractional difference image; in the end, an exact threshold should be determined according to the difference image. In order to avoid some false change and noises of the initial fractional difference image, a suitable threshold should be set. In the light of the intensity change values in the image are consistent with the distribution of Gauss mixture, the expectation maximization (EM) algorithm and Bayes discriminant criterion are both used to find out the best threshold of each land cover change. Once the threshold value is set, fraction changes larger than the value will be considered to be correct; other changes will be regarded as zero. The proposed method is compared with two traditional methods in both of the simulated and real experiments. The result demonstrates that our method can extract more precise changed information with high stability. |
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