基于形状特征与容错宽度Hough变换的影像道路提取
A Method of Road Extraction from Remote Sensing Images Based on Shape Features and Width-Tolerant Hough Transform
DOI: 10.12677/JISP.2014.32006, PDF,   
作者: 张国英, 赵 鹏, 宋科科：中国矿业大学(北京)机电与信息工程学院计算机科学与技术系，北京
关键词: 道路提取；遥感影像分割；形状特征；Hough变换；Road Extraction； Remote Sensing Image Segmentation； Shape Features； Hough Transform
摘要: 提出一种基于分割后图像形状特征并结合改进Hough变换进行道路提取的方法。该方法首先对图像进行分割，对分割结果使用形状特征进行道路段的初步筛选，使用改进后的Hough变换方法对目标进行筛分、合并和形态优化，完成遥感影像的道路网提取过程。提出的方法能适用于复杂的高分辨率遥感影像中道路段的提取。经过实验分析和比较证明：该方法对于路面灰度均匀性较差及路况复杂、干扰物较多的图片，都达到了较好的效果。
Abstract: Road extraction from high-resolution remote sensing image is an important and difficult task. The road-extraction method, which uses the integration shape features and the improved Hough transform, is proposed in this paper. Firstly, the image is segmented, and then the linear and curve roads are obtained by using several object shape features. Secondly, the step of road extraction is using the improved Hough transform method to deal with the road targets. Finally, the extracted roads are regulated by combining the edge information. In experiments, the images including the better gray uniform of road and the worse illuminated of road surface were chosen, and the results prove that the method of this study is promising.
文章引用：张国英, 赵鹏, 宋科科. 基于形状特征与容错宽度Hough变换的影像道路提取[J]. 图像与信号处理, 2014, 3(2): 29-38. http://dx.doi.org/10.12677/JISP.2014.32006

参考文献

[1] Mena, J.B. (2003) State of the Art on Automatic Road Extraction for GIS Update: A Novel Classification. Pattern Recognition Letters, 24, 3037-3058.
[2] 吴亮, 胡云安 (2010) 遥感图像自动道路提取方法综述. 自动化学报, 7, 912-922.
[3] 苗则朗, 史文中, 张华 (2013) 一种高分辨率影像道路中心线提取算法. 中国矿业大学学报, 5, 258-264.
[4] Trinder, J. and Li, H. (1995) Semi-Automatic Feature Extraction by Snakes. In: Grun, A., Kubler, O. and Agouris, P., Eds., Automatic Extraction of Man-Made Objects from Aerial and Space Images (1), Birkhauser Verlag, Basel, 95-104.
[5] Trinder, J.C., Wang Y.D., et al. (1997) Artificial Intelligence in 3D feature Extraction. In: Automatic Extraction of Man-Made Objects from Aerial and Space Images (2), Birkhaeuser Verlag, Basel, 257-265.
[6] Ton, J, Jain A.K. and Enslin W.R. (1989) Automatic Road Identification and Labeling in Landsat 4 TM Images. Photogrammetric, 43, 257-276.
[7] Barzohar, M. and Cooper, D.B. (1996) Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation. IEEE Transform on Pattern Analysis and Machine Intelligence, 18, 707-721.
[8] Hinz, S. and Baumgartner, A. (2003) Automatic Extraction of Urban Road Networks from Multi-View Aerial Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 83-98.
[9] Hinz, S. and Baumgartner, A. (2002) Urban Road Net Extraction Integrating Internal Evaluation Models. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34, 163-168.
[10] 龙辉 (2006) 高分辨率光学卫星影像城市道路识别方法研究. 博士学位论文, 中国科学院研究生院(遥感应用研究所), 北京.
[11] 雷小奇, 王卫星, 赖均 (2009) 一种基于形状特征进行高分辨率遥感影像道路提取方法. 测绘学报, 5, 457-465.
[12] 何建农, 钟顺虹 (2013) 基于标值点过程改进的遥感道路提取辛酸发. 计算机工程与应用, 17, 150-158.
[13] 梁栋, 刘书丽 (2012) 基于NSCT与形状特征的遥感影像道路提取. 华中科技大学学报(自然科学版), 3, 9-12.
[14] 段汝娇, 赵伟 (2010) 一种基于改进Hough变换的直线快速检测算法. 仪器仪表学报, 12, 2774-2779.
[15] 刘小丹, 刘岩 (2012) 基于Hough变换和路径形态学的城区道路提取. 计算机工程, 6, 527-531.
[16] Ayaou, T. and Mourad, B. (2013) Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture. International Journal of Computer Applications, 73, 5-7.