| SVM多窗口纹理土地利用信息提取技术 |
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| 引用本文： | 张伐伐,李卫忠,卢柳叶,张青峰,康乐.SVM多窗口纹理土地利用信息提取技术[J].遥感学报,2012,16(1):67-78. |
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| 作者姓名： | 张伐伐 李卫忠 卢柳叶 张青峰 康乐 |
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| 作者单位： | 西北农林科技大学 林学院，陕西 杨凌 712100;西北农林科技大学 林学院，陕西 杨凌 712100;西北农林科技大学 资环学院，陕西 杨凌 712100;西北农林科技大学 资环学院，陕西 杨凌 712100;西北农林科技大学 林学院，陕西 杨凌 712100 |
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| 基金项目： | 引进国际先进农业科学技术计划(948计划)(编号: 2009-04-45) |
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| 摘 要： | 针对单一窗口纹理分类时地物破碎,分类精度不高等问题,提出了一种基于支持向量机多窗口纹理的遥感图像分类方法。该方法在对SPOT5遥感影像进行纹理特征提取的基础上,构建了结合多窗口纹理的SVM模型。以陕西省佛坪县长角坝乡为试验区,利用此模型对该区域的土地利用类型进行分类研究,并将分类结果与单一窗口纹理SVM分类和单元数据(光谱)SVM分类结果进行了比较分析。结果表明:多窗口纹理参与的土地利用分类总精度达到85.33%,比单一窗口纹理分类提高了13.11%,而与单元数据SVM分类相比提高了近24.10%,取得了较好的分类效果,有效地解决了单一窗口纹理分类时地物破碎、分类精度不高等问题。
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| 关 键 词： | 支持向量机 纹理特征 土地利用 单一窗口纹理 多窗口纹理 |
| 收稿时间： | 2010/11/30 0:00:00 |
| 修稿时间： | 4/5/2011 12:00:00 AM |
Technologies of extracting land utilization information based on SVM method with multi-window texture |
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| ZHANG Faf,LI Weizhong,LU Liuye,ZHANG Qingfeng and KANG Le.Technologies of extracting land utilization information based on SVM method with multi-window texture[J].Journal of Remote Sensing,2012,16(1):67-78. |
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| Authors: | ZHANG Faf LI Weizhong LU Liuye ZHANG Qingfeng and KANG Le |
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| Institution: | College of Forestry, Northwest A&F University, Yangling, 712100, China;College of Forestry, Northwest A&F University, Yangling, 712100, China;College of Resources and Environment, Northwest A&F University, Yangling, 712100, China;College of Resources and Environment, Northwest A&F University, Yangling, 712100, China;College of Forestry, Northwest A&F University, Yangling, 712100, China |
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| Abstract: | In order to overcome the problem of fragmentation of ground objects and low accuracy in the single window texture classification, we present a new method of classification using SVM based on multi-window texture, using the Changjiaoba town of Foping county in Shaanxi Province as the test area. First we established the SVM classification model combined with texture analysis based on texture extraction from SPOT 5 remote sensing image. Then we used the model to classify and analyze the types of land use in the area by comparing it with single window texture classification and single data source (spectrum) SVM classification. The research result showed an overall accuracy for multi-window texture classification of 85.33%, which was 13.11% higher than the single window texture classification and 24.10% than single data source (spectrum) SVM. Therefore, we conclude that the method is effective and could solve the problem of fragmentation of ground objects and low accuracy in the single window texture classification. |
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| Keywords: | support vector machine texture feature land use single window texture multi-window texture |
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