| 基于GF-5高光谱特征分析的水体提取方法研究 |
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| 引用本文： | 沈聪颖,甘淑,李新澳,冯鸿能.基于GF-5高光谱特征分析的水体提取方法研究[J].全球定位系统,2021,46(5):117-122. |
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| 作者姓名： | 沈聪颖 甘淑 李新澳 冯鸿能 |
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| 作者单位： | 昆明理工大学 国土资源工程学院，昆明 650093 |
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| 基金项目： | 国家自然科学基金项目（41861054）；云南省自然科学基金(2015FA016) |
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| 摘 要： | 针对山区水体、山体阴影与裸地等地类光谱混淆性，基于高分五号(GF-5)影像数据，结合高光谱特征分析构建了山区水体决策树提取模型. 先对水体和相关干扰地类进行高光谱特征分析实现特征波段选取，应用单波段阈值法、多波段谱间关系法、归一化水指数(NDWI)法进行提取实验. 通过比较以上实验不足之处，提出了单波段阈值法与构建的阴影水体指数(SWI)相结合的决策树水体提取模型，以Google Earth高清影像为参考结合实地采样得到的混淆矩阵进行精度评价. 实验结果表明:单波段阈值法与NDWI法易将山体阴影识别为水体，受裸地影响较小；多波段谱间关系法对山体阴影有一定抑制作用，受小面积裸地影响；决策树提取模型能有效抑制山体阴影和裸地影响提取完整水体. 其总体精度为89.39%，Kappa系数为0.82，显著提升了山区水体提取精度.
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| 关 键 词： | 高分五号(GF-5) 高光谱特征分析 阴影水体指数 决策树 |
| 收稿时间： | 2021-03-09 |
Research on water extraction method based on GF-5 hyperspectral feature analysis |
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| Institution: | School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China |
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| Abstract: | In view of the spectral confusion between mountain water body, mountain shadow and bare land, a decision tree extraction model for mountain water body is constructed based on Gao Fen-5(GF-5) image data combined with hyperspectral feature analysis. First, perform hyperspectral feature analysis on the water body and related interference types to realize the selection of feature bands, apply single-band threshold method, multiple-band spectral relationship method, and NDWI method to extract experiments. By comparing the deficiencies of the above experiments, a single-band threshold is proposed. The decision tree water body extraction model combined with the constructed shadow water index (SWI) is used to evaluate the accuracy of the confusion matrix obtained by using Google Earth high-definition images as a reference and on-site sampling. The experimental results show that the single-band threshold method and NDWI method can easily identify mountain shadow as water body and are less affected by bare land; the multiple-band spectrum relationship method has a certain inhibitory effect on mountain shadow and is affected by small areas of bare land; decision tree model can effectively suppress the influence of mountain shadow and bare land to extract complete water body. The overall accuracy is 89.39%, and the Kappa coefficient is 0.82, which significantly improves the extraction accuracy of mountain water body. |
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