道路综合特征下高分辨率遥感影像的提取
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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。
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.
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.
引文
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[13]杨志刚,张世奇.高分辨率遥感影像下线特征提取方法比较[J].测绘通报,2015(8):66-69.
[14]雷小奇,王卫星,赖均.一种基于形状特征进行高分辨率遥感影像道路提取方法[J].测绘学报,2009,38(5):457-465.
[15]胡张武.高分辨率遥感影像道路信息提取方法研究[J].测绘通报,2011(8):13-16.
[16]胡华龙,吴冰,黄邵美.结合Gabor纹理与几何特征的高分辨率遥感影像城区道路提取方法[J].测绘科学技术学报,2015,32(4):395-400.
[17]夏春林,张静,褚廷有.基于高分辨率城区遥感影像的道路半自动提取方法研究[J].测绘科学,2008,33(5):140-142.
[2]董立菊,于戈.一种有效的图像二值化方法[J].东北大学学报,2004,25(12):1149-1152.
[3]王大鹏,姜挺.基于模糊算子理论的道路半自动提取[J],测绘科学技术学报,2004,21(3):187-189.
[4]HARALICK R M,SHANMYGAM K,DINSTEIN I H.Textual Features for Image Classification[J].IEEE Transactions on Systems,Man and Cybernetics,1973,3(6):610-621.
[5]OHANIAN P P,DUBES R C.Performance Evaluation for Four Classes of Texture Features[J].Pattern Recognition,1992,25(8):819-833.
[6]HARALICK R M.Statistical and Structural Approaches to Texture[J].Proceeding of the IEEE,1979,67(5):768-804.
[7]BARALDI A,PAMIGGIANI F.An Investigation of Textural Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):293-304.
[8]MA Hongbin,ZHANG Cun,YANG Shengfei,et al.Object-oriented Information Extraction from High Resolution Remote Sensing Imagery[C]∥Proceedings of 2nd International Congress on Image and Signal Processing.Tianjin:IEEE,2009.
[9]MANJUNATH B S,MA W Y.Texture Features for Browsing and Retrieval of Image Data[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):837-842.
[10]袁方,孟增辉,于戈.对K-means聚类算法的改进[J].计算机工程与应用,2004,40(36):177-178.
[11]雷小奇,王卫星,赖均.一种基于形状特征进行高分辨率遥感影像道路提取方法[J].测绘学报,2009,38(5):457-465.
[12]朱晓铃,邬群勇.基于高分辨率遥感影像的城市道路提取方法研究[J].测绘通报,2012(S0):1-4.
[13]杨志刚,张世奇.高分辨率遥感影像下线特征提取方法比较[J].测绘通报,2015(8):66-69.
[14]雷小奇,王卫星,赖均.一种基于形状特征进行高分辨率遥感影像道路提取方法[J].测绘学报,2009,38(5):457-465.
[15]胡张武.高分辨率遥感影像道路信息提取方法研究[J].测绘通报,2011(8):13-16.
[16]胡华龙,吴冰,黄邵美.结合Gabor纹理与几何特征的高分辨率遥感影像城区道路提取方法[J].测绘科学技术学报,2015,32(4):395-400.
[17]夏春林,张静,褚廷有.基于高分辨率城区遥感影像的道路半自动提取方法研究[J].测绘科学,2008,33(5):140-142.