| 基于改进SVM的新疆北部地区积雪面积反演研究——以天山山区中段为例 |
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| 引用本文： | 瞿娟,丁建丽,孙永猛.基于改进SVM的新疆北部地区积雪面积反演研究——以天山山区中段为例[J].资源科学,2013,35(2):422-429. |
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| 作者姓名： | 瞿娟 丁建丽 孙永猛 |
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| 作者单位： | 新疆大学资源与环境科学学院绿洲生态教育部重点实验室,乌鲁木齐,830046 |
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| 基金项目： | 国家自然科学基金项目:“新疆北部地区积雪冰冻灾后融水模型构建与农业春汛预警遥感研究”(编号:41161059);国家自然科学基金(编号:41161063);教育部新世纪优秀人才支持计划资助。 |
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| 摘 要： | 积雪面积是融雪径流模型中变量数据输入之一,准确的获取雪盖范围是进行流域尺度融雪水文过程研究的关键,在水资源管理及洪水预报中具有重要意义.本文以天山山区中段为例,利用MODIS数据,提出了结合混合光谱分解的积雪分量及灰度共生矩阵提取的纹理特征的SVM分类方法,对研究区积雪面积信息提取进行了研究.结果表明:通过利用混合光谱分解的积雪分量作为SVM的特征输入,总体分类精度比传统SVM分类结果有了一些提高.同时考虑结合基于灰度共生矩阵提取的纹理特征用于分类中,总体精度比传统SVM方法提高了1.081％,制图精度达到了99.01％.本文提出的分类方法能够适应特征组合之间的非线性关系,从而能提供更多的区域地物空间分布信息,能够调整无样本地表类型地区的积雪面积反演,对今后的融雪水文过程研究有重要意义.
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| 关 键 词： | 积雪面积 混合光谱分解 灰度共生矩阵 支持向量机 新疆北部地区 |
Improved SVM for Extracting Snow Cover in Northern Xinjiang |
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| QU Juan,DING Jianli and SUN Yongmeng.Improved SVM for Extracting Snow Cover in Northern Xinjiang[J].Resources Science,2013,35(2):422-429. |
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| Authors: | QU Juan DING Jianli and SUN Yongmeng |
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| Institution: | Key Laboratory of Oasis EcosystemXinjiang Universityof Education Ministry of China, Resource and Environment institute of Xinjiang University, Urumqi 830046, China;Key Laboratory of Oasis EcosystemXinjiang Universityof Education Ministry of China, Resource and Environment institute of Xinjiang University, Urumqi 830046, China;Key Laboratory of Oasis EcosystemXinjiang Universityof Education Ministry of China, Resource and Environment institute of Xinjiang University, Urumqi 830046, China |
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| Abstract: | Snow is the most active natural land surface factor and has implications for the global climate and hydrological environment. In Xinjiang, seasonal snow accumulation and melt are important to water resource management and the sustainable development of oases. Snow area identification and mapping comprise fundamental environment research in cold, arid and semi-arid regions. Here, we extract snow area from MOD02 HKM images, one of the three MODIS L1B products (MOD02 QKM, MOD02 HKM, MOD02 1KM) at 500m spatial resolution for the Tianshan Mountains. We test the SVM method combined with a snow component of spectral mixture analysis and texture feature extraction by GLCM using MODIS data to extract snow area information. Results indicate that using the snow component of spectral mixture analysis achieves superior results compared to traditional SVM classification results; classification accuracy was increased by 0.2702%. Combining the texture features extracted with GLCM for classification improved overall accuracy by 1.081% and mapping accuracy of 99.01%. This suggests that the SVM method combined with the snow component of spectral mixture analysis and texture feature extraction by GLCM is effective for snow area extraction using MODIS data at low spatial resolution. The classification method proposed in this paper can adapt to the nonlinear relationship between features. By adding spectral feature vectors with a significant relationship to snow area, and texture features like the SVM input vector, our method adjusts snow area extraction when land cover types lack training samples and improves overall accuracy. |
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| Keywords: | Snow cover Spectral mixture analysis Gray level cooccurrence matrice Support vector machine Northern Xinjiang |
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