| 道路综合特征下高分辨率遥感影像的提取 |
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| 引用本文： | 魏国武,王琦,张阳阳,陈永生.道路综合特征下高分辨率遥感影像的提取[J].测绘通报,2017,0(8):31-35. |
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| 作者姓名： | 魏国武 王琦 张阳阳 陈永生 |
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| 作者单位： | 1. 辽宁地质工程职业学院, 辽宁 丹东 118008;2. 东北大学资源与土木工程学院, 辽宁 沈阳 110819 |
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| 基金项目： | 国家自然科学基金青年项目 |
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| 摘 要： | 针对在高分辨率遥感影像中如何提高道路信息提取的准确度和信息量这一问题，通过对影像光谱和纹理特征的分析，将影像特征按照2种光谱特征和3种纹理特征进行分类，进而改善传统的图像分割方法，选择灰度级数和像素对的相对方向、距离和窗口大小作为参数，再通过灰度共生矩阵运算获取影像的纹理信息，通过对这些纹理特征的综合比较分析，最后确定角二阶矩、熵和对比度作为道路纹理特征统计量；再通过对图像像元分析比较，将图像像元标准差和灰度均值作为道路信息提取的光谱特征；在对道路综合特征分析基础上，再通过对遥感图像几何特征分析，最后利用数学形态学的开运算、闭运算、腐蚀、细化等模型算法对遥感图像进行精细化处理，得到道路提取较好的结果。该方法可用于复杂路况的道路信息提取。
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| 关 键 词： | 高分辨率遥感影像 纹理特征 光谱特征 数学形态学 道路提取 |
| 收稿时间： | 2016-12-19 |
The Extraction Method of High Resolution Remote Sensing Image Based on Road Comprehensive Feature |
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| WEI Guowu,WANG Qi,ZHANG Yangyang,CHEN Yongsheng.The Extraction Method of High Resolution Remote Sensing Image Based on Road Comprehensive Feature[J].Bulletin of Surveying and Mapping,2017,0(8):31-35. |
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| Authors: | WEI Guowu WANG Qi ZHANG Yangyang CHEN Yongsheng |
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| Institution: | 1. Liaoning Geology Engineering Vocational College, Dandong 118008, China;2. School of Resources and Civil Engeering, Northeastern University, Shenyang 110819, China |
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| Abstract: | Aimed at improving roads in high resolution remote sensing image information extraction accuracy and the amount of information, this paper focuses on improving the image from the segmentation, and introduces texture and spectral characteristics of the image.An image clustering segmentation method based on combined features is proposed.We divide integrated features into three kinds of texture features and spectral characteristics.By selecting the size of the window from the gray levels and the relative orientation of the pixels on the four parameters, gray level dependence matrix extracts image texture features, after contrast five kinds of suitable texture characteristics for remote sensing image, this paper chooses contrast, angle second order moment and entropy as the texture characteristics, and chooses average gray image pixel and standard deviation as spectral features.Finally, based on segmentation image, which segmented by the comprehensive features of road, using image geometric characteristics and mathematical morphology such as open operation, corrosion, closed operation, refined, and the image processing algorithm to get the final road extraction results.The experimental results show that the method can be used to complex road information extraction. |
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| Keywords: | high-resolution remote sensing images the texture characteristics spectral features mathematical morphology road extraction |
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