| 基于EnMAP-Box的遥感图像分类研究 |
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| 引用本文： | 林海晏,岳彩荣,吴晓晖,胥辉,郑欣.基于EnMAP-Box的遥感图像分类研究[J].西南林业大学学报,2014(2):67-71. |
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| 作者姓名： | 林海晏 岳彩荣 吴晓晖 胥辉 郑欣 |
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| 作者单位： | [1]西南林业大学林学院,云南昆明650224 [2]首都体育学院现代教育技术中心,北京100086 |
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| 基金项目： | 基金项目：国家公益性行业科研专项（200904045）资助;国家自然基金项目（31260156）资助. |
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| 摘 要： | 采用2007年6月云南省勐腊县TM遥感数据，利用EnMAP-box进行了支持向量机的图像分类研究，以网格搜索法寻找最优参数，在设定的范围内，求得了最优C和g参数，用此参数进行支持向量机的遥感图像土地覆盖分类。结果表明：SVM方法较最大似然分类方法具有较高的分类精度，特别是阔叶林和橡胶林的精度明显优于最大似然分类方法；对于面积较小的次要类型，2种分类方法的精度基本保持一致；SVM的总体精度相对于最大似然分类提高了11.9％。
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| 关 键 词： | 支持向量机 EnMAP—box 网格搜索法 遥感图像分类 |
Remote Sensing Image Classification by EnMAP-Box Model |
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| LIN Hai-yan,YUE Cai-rong,WU Xiao-hui,XU Hui,ZHENG Xin.Remote Sensing Image Classification by EnMAP-Box Model[J].Journal of Southwest Forestry University,2014(2):67-71. |
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| Authors: | LIN Hai-yan YUE Cai-rong WU Xiao-hui XU Hui ZHENG Xin |
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| Institution: | 1. College of Forestry, Southwest Forestry University, Kunming Yunnan 650224, China; 2. New Media Service Center, Capital University of Physical Education and Sports, Beijing 100086, China) |
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| Abstract: | Image classification of the TM remote sensing data of Mengla County,Yunnan Province in June of 2007 was conducted by EnMAP-box model with the support vector machine （SVM）,attempting to search for the optimal parameters by grid search.The optimal C and g parameters were obtained within a set range,and the land cover classification was done by SVM with the optimized parameters and the remote sensing image.The results showed that the classification accuracy of SVM classifier was higher than that of the regular Maximum Likelihood Classifier （MLC）,especially for the broadleaved forests and rubber plantations.The classification accuracy of the two methods would be similar for smaller secondary land types.Comparatively speaking,the overall accuracy of the SVM was 1 1 .9% higher than that of MLC. |
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| Keywords: | support vector machine （SVM） EnMAP-box Model grid search remote sensing image classification |
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