基于P-N学习的高分遥感影像道路半自动提取方法
|
摘要
基于模板匹配的道路跟踪是半自动提取道路的主要方法。然而场景中地物干扰和道路宽度的变化降低了模板匹配的稳定性;另外,道路跟踪失败后缺乏重检测机制,使得道路提取过程中人机交互频繁。针对以上问题,提出了一种基于P-N(positive-negative)学习的高分遥感影像道路半自动提取方法。该方法由道路跟踪、检测和学习构成,关键是采用了P-N学习的策略迭代的训练分类器,通过纠正违反结构约束的样本分类结果来提高分类器性能。实验使用了不同场景下的城区高分遥感影像,与经典的模板匹配和在线学习的道路跟踪方法进行了比较。实验结果表明该方法在道路提取的精度和稳定性方面均有提升。
The road tracking method based on template matching is one major semi-automatic road extraction method.However,template matching is sensitive to complexity of road scenes and variance in road width.In addition,road extraction requires frequent human-computer interaction while road tracking encounters failure without a mechanism for re-detection.To solve these problems,one semiautomatic road extraction method using high resolution remote sensing image based on P-N learning is proposed.It consists of road tracking,detecting and learning.In order to improve the stability of road detection,we train a classifier with an iterative P-N learning strategy.The performance of classifier is improved by correcting sample labeling under structural constraints.In experiments,the proposed method and three classical methods are tested on high-resolution remote sensing images of different scenes.Comparitive results show proposed method'improves precision and stability of road extraction.
The road tracking method based on template matching is one major semi-automatic road extraction method.However,template matching is sensitive to complexity of road scenes and variance in road width.In addition,road extraction requires frequent human-computer interaction while road tracking encounters failure without a mechanism for re-detection.To solve these problems,one semiautomatic road extraction method using high resolution remote sensing image based on P-N learning is proposed.It consists of road tracking,detecting and learning.In order to improve the stability of road detection,we train a classifier with an iterative P-N learning strategy.The performance of classifier is improved by correcting sample labeling under structural constraints.In experiments,the proposed method and three classical methods are tested on high-resolution remote sensing images of different scenes.Comparitive results show proposed method'improves precision and stability of road extraction.
引文
[1]Lin Xiangguo,Zhang Jixian,Li Haitao,et al.Semiautomatic Extraction of Ribbon Road from High Resolution Remotely Sensed Imagery by a T-Shaped Template Matching[J].Geomatics and Information Science of Wuhan University,2009,34(3):293-296(林祥国,张继贤,李海涛,等.基于T型模板匹配半自动提取高分辨率遥感影像带状道路[J].武汉大学学报·信息科学版,2009,34(3):293-296)
[2]Hu X,Zhang Z,Tao C V.A Robust Method for Semi-automatic Extraction of Road Centerlines Using a Piecewise Parabolic Model and Least Square Template Matching[J].Photogrammetric Engineering&Remote Sensing,2004,70(12):1 393-1 398
[3]Miao Z,Wang B,Shi W,et al.A Semi-automatic Method for Road Centerline Extraction from VHR Images[J].IEEE Geoscience and Remote Sensing Letters,2014,11:1 856-1 860
[4]Zhang J,Lin X,Liu Z,et al.Semi-automatic Road Tracking by Template Matching and Distance Transformation in Urban Areas[J].International Journal of Remote Sensing,2011,32(23):8 331-8 347
[5]Yu Jie,Yu Feng,Zhang Jing,et al.High Resolution Remote Sensing Image Road Extraction Combining Region Growing and Road-Unit[J].Geomatics and Information Science of Wuhan University,2013,38(7):761-764(余洁,余峰,张晶,等.结合区域生长与道路基元的高分辨率遥感影像道路提取[J].武汉大学学报·信息科学版,2013,38(7):761-764)
[6]Vosselman G,De Knecht J.Road Tracing by Profile Matching and Kalman Filtering[M]//Automatic Extraction of Man-Made Objects from Aerial and Space Images,Birkhuser:Springer,1995
[7]Fu Gang,Zhao Hongrui,Li Cong,et al.A Method by Improved Circular Projection Matching of Tracking Twisty Road from Remote Sensing Imagery[J].Acta Geodaetica et Cartographica Sinica,2014,43(7):724-730,738(傅罡,赵红蕊,李聪,等.曲折道路遥感影像圆投影匹配改进追踪法[J].测绘学报,2014,43(7):724-730,738)
[8]Meng Fan,Fang Shenghui.Quasi-automatic Extraction of Zonal Roads from Remote Sensing Images Using Template Matching and BSnake Model[J].Geomatics and Information Science of Wuhan University,2012,37(1):39-42(孟樊,方圣辉.利用模板匹配和BSnake算法准自动提取遥感影像面状道路[J].武汉大学学报·信息科学版,2012,37(1):39-42)
[9]Zhou J,Cheng L,Bischof W F.Online Learning With Novelty Detection in Human-Guided Road Tracking[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(12):3 967-3 977
[10]Achanta R,Shaji A,Smith K,et al.SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2 274-2 282
[11]Movaghati S,Moghaddamjoo A,Tavakoli A.Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(7):2 807-2 817
[12]Breiman L.Random Forests[J].Machine Learning,2001,45(1):5-32
[13]Wiedemann C,Heipke C,Mayer H,et al.Automatic Extraction and Evaluation of Road Networks from MOMS-2P Imagery[J].International Archives of Photogrammetry and Remote Sensing,1998,32(1):285-291
[2]Hu X,Zhang Z,Tao C V.A Robust Method for Semi-automatic Extraction of Road Centerlines Using a Piecewise Parabolic Model and Least Square Template Matching[J].Photogrammetric Engineering&Remote Sensing,2004,70(12):1 393-1 398
[3]Miao Z,Wang B,Shi W,et al.A Semi-automatic Method for Road Centerline Extraction from VHR Images[J].IEEE Geoscience and Remote Sensing Letters,2014,11:1 856-1 860
[4]Zhang J,Lin X,Liu Z,et al.Semi-automatic Road Tracking by Template Matching and Distance Transformation in Urban Areas[J].International Journal of Remote Sensing,2011,32(23):8 331-8 347
[5]Yu Jie,Yu Feng,Zhang Jing,et al.High Resolution Remote Sensing Image Road Extraction Combining Region Growing and Road-Unit[J].Geomatics and Information Science of Wuhan University,2013,38(7):761-764(余洁,余峰,张晶,等.结合区域生长与道路基元的高分辨率遥感影像道路提取[J].武汉大学学报·信息科学版,2013,38(7):761-764)
[6]Vosselman G,De Knecht J.Road Tracing by Profile Matching and Kalman Filtering[M]//Automatic Extraction of Man-Made Objects from Aerial and Space Images,Birkhuser:Springer,1995
[7]Fu Gang,Zhao Hongrui,Li Cong,et al.A Method by Improved Circular Projection Matching of Tracking Twisty Road from Remote Sensing Imagery[J].Acta Geodaetica et Cartographica Sinica,2014,43(7):724-730,738(傅罡,赵红蕊,李聪,等.曲折道路遥感影像圆投影匹配改进追踪法[J].测绘学报,2014,43(7):724-730,738)
[8]Meng Fan,Fang Shenghui.Quasi-automatic Extraction of Zonal Roads from Remote Sensing Images Using Template Matching and BSnake Model[J].Geomatics and Information Science of Wuhan University,2012,37(1):39-42(孟樊,方圣辉.利用模板匹配和BSnake算法准自动提取遥感影像面状道路[J].武汉大学学报·信息科学版,2012,37(1):39-42)
[9]Zhou J,Cheng L,Bischof W F.Online Learning With Novelty Detection in Human-Guided Road Tracking[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(12):3 967-3 977
[10]Achanta R,Shaji A,Smith K,et al.SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2 274-2 282
[11]Movaghati S,Moghaddamjoo A,Tavakoli A.Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(7):2 807-2 817
[12]Breiman L.Random Forests[J].Machine Learning,2001,45(1):5-32
[13]Wiedemann C,Heipke C,Mayer H,et al.Automatic Extraction and Evaluation of Road Networks from MOMS-2P Imagery[J].International Archives of Photogrammetry and Remote Sensing,1998,32(1):285-291