| 基于多特征融合与典型降维方法的高光谱影像分类 |
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| 引用本文： | 窦世卿,陈治宇,徐勇,郑贺刚,苗林林,宋莹莹.基于多特征融合与典型降维方法的高光谱影像分类[J].测绘通报,2022,0(4):32-36+50. |
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| 作者姓名： | 窦世卿 陈治宇 徐勇 郑贺刚 苗林林 宋莹莹 |
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| 作者单位： | 桂林理工大学测绘地理信息学院, 广西 桂林 541006 |
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| 基金项目： | 国家自然科学基金(42061059)；;广西自然科学基金(2020JJB150025)； |
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| 摘 要： | 高光谱影像的冗余信息给影像的分类效果带来一定的负面影响。本文利用CB法(CfsSubsetEval评估器结合Best-First搜索策略)与PCA变换两种降维方法，分别结合随机森林分类器对4种多特征融合方案(共8种组合)进行高光谱影像分类对比，基于分类的总体精度、Kappa系数探究提高高光谱影像分类的最佳组合方法。结果表明:①多特征融合可提升高光谱影像的分类效果，两种降维方法的分类精度均随地理特征、纹理特征、指数特征的加入而逐渐提高。②两种降维方法中，经CB法降维后的分类精度均比通过PCA变换降维的分类精度高。在构造的8种组合中，基于所有特征信息(光谱特征、地理特征、纹理特征、指数特征)的CB法分类精度最高，其总体精度为98.01%；Kappa系数为0.969 9。
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| 关 键 词： | 高光谱影像 影像分类 降维 特征融合 随机森林 |
| 收稿时间： | 2021-04-28 |
Hyperspectral image classification based on multi-feature fusion and dimensionality reduction algorithms |
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| DOU Shiqing,CHEN Zhiyu,XU Yong,ZHENG Hegang,MIAO Linlin,SONG Yingying.Hyperspectral image classification based on multi-feature fusion and dimensionality reduction algorithms[J].Bulletin of Surveying and Mapping,2022,0(4):32-36+50. |
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| Authors: | DOU Shiqing CHEN Zhiyu XU Yong ZHENG Hegang MIAO Linlin SONG Yingying |
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| Institution: | College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China |
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| Abstract: | Hyperspectral images does exist redundant information, which brings certain side-effects on image classification. In this study, two dimensionality reduction algorithms, the CB method (CfsSubsetEval evaluator combines Best-First search strategies) and the PCA, and four multi-feature fusion combinations are proposed to construct eight schemes. The eight schemes combining with RF(random forest) classifier are then applied to classily hyperspectral images, and the best scheme for hyperspectral image classification are selected on the bases of the classification accuracy and Kappa coefficient. The results show that:①Multi-feature fusion can improve the classification accuracy of hyperspectral images, the classification accuracy of the hyperspectral image increases with considering geographic characteristics, texture characteristics, and exponential features gradually both in the two dimensionality reduction algorithms.②Considering the two dimensionality reduction algorithms, the classification accuracy based on CB reduction is generally higher than that of PCA dimensionality reduction. In terms of the classification accuracy based on eight schemes, the CB method with spectrum information, geographic characteristics, texture characteristics, and exponential features has the highest classification accuracy with 98.01% of overall classification accuracy, and 0.969 9 of Kappa coefficient. |
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| Keywords: | hyperspectral image image classification dimensionality reduction feature fusion random forest |
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