基于多时相DOM的线性文化遗产自动化监测
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摘要
提出一种将遥感技术应用到线性文化遗产的自动化监测方法,即基于多时相数字正射影像图(digital orthophoto map,DOM),运用多波段差值法以及面向对象分类监测方法,对线性文化遗产(以苏州宝带桥段遗产保护区为例)进行了自动化监测研究。实验结果表明,两种方法均可用于线性文化遗产自动化监测,但面向对象的分类监测法对连续的、大面积的地物变化更有效,多波段差值法对离散的地物变化更加敏感。该研究为我国线性文化遗产自动化监测与保护提供了思路,具有一定的推广价值。
In this paper, remote sensing technology is applied to the monitoring of the dynamic changes of cultural heritage. Based on multi-temporal digital orthophoto map(DOM) images, the multi-band difference method and the object-oriented post-classification change detection method are used to monitor the dynamic changes of the Precious Belt Bridge in Suzhou. Experimental results show that both methods can be applied to the dynamic change detection of cultural heritage, and the object-oriented post-classification change detection method is more effective for continuous and large-scale change of ground objects, while multi-band difference method is more sensitive to discrete ground object changes. The research results in this paper can provide ideas for the intelligent monitoring and protection of the cultural heritage in our country, which is have certain promotion value.
In this paper, remote sensing technology is applied to the monitoring of the dynamic changes of cultural heritage. Based on multi-temporal digital orthophoto map(DOM) images, the multi-band difference method and the object-oriented post-classification change detection method are used to monitor the dynamic changes of the Precious Belt Bridge in Suzhou. Experimental results show that both methods can be applied to the dynamic change detection of cultural heritage, and the object-oriented post-classification change detection method is more effective for continuous and large-scale change of ground objects, while multi-band difference method is more sensitive to discrete ground object changes. The research results in this paper can provide ideas for the intelligent monitoring and protection of the cultural heritage in our country, which is have certain promotion value.
引文
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[2] Zhang Liangpei, Wu Chen. Advanced and Future Development of Change Detection for Multi-temporal Remote Sensing Imagery[J].Acta Geodaetica et Cartographica Sinica,2017,46(10): 1 447-1 459 (张良培,武辰.多时相遥感影像变化监测的现状与展望[J].测绘学报,2017,46(10):1 447-1 459)
[3] Yin Shoujing, Wu Chuanqing, Wang Qiao, et al. Review of Change Detection Methods Using Multi-Temporal Remotely Sensed Images[J].Spectroscopy and Spectral Analysis,2013,33(12):3 339-3 342(殷守敬,吴传庆,王桥,等.多时相遥感影像变化监测方法研究进展综述[J].光谱学与光谱分析,2013,33(12):3 339-3 342)
[4] Sun Yue,Wang Hongqi,Li Feng,et al. Elastic Re- gistration of Remote Sensing Image for Change Detection[J].Geomatics and Information Science of Wuhan University, 2018,43(1):53-59 (孙越,王宏琦,李峰,等.面向变化检测的遥感影像弹性配准方法[J].武汉大学学报·信息科学版,2018,43(1):53-59)
[5] Tong Guofeng, Li Yong, Ding Weili, et al.Review of Remote Sensing Image Change Detection[J].Journal of Image and Graphics,2015,20(12): 1 561-1 571(佟国峰,李勇,丁伟利,等.遥感影像变化监测算法综述[J].中国图象图形学报,2015,20(12):1 561-1 571)
[6] Sun Xiaoxia, Zhang Jixian, Yan Qin, et al. A Summary on Current Techniques and Prospects of Remote Sensing Change Detection[J]. Remote Sensing Information,2011,26(1):119-123(孙晓霞,张继贤,燕琴,等. 遥感影像变化监测方法综述及展望[J].遥感信息,2011,26(1):119-123)
[7] Zhang Yongmei, Li Lipeng, Jiang Ming, et al. Change Detection Method for Buildings Based on Pixel-Level and Feature-Level[J]. Computer Science, 2013,40(1):286-293(张永梅,李立鹏,姜明,等.综合像素级和特征级的建筑物变化检测方法[J].计算机科学,2013,40(1):286-293)
[8] Zhang Yiming.Research on Change Detection Based on Unmanned Aerial Vehicles Serial Images[D]. Zhengzhou:Information Engineering University,2008(张一鸣.基于无人机序列图像的变化监测技术研究[D].郑州:信息工程大学,2008)
[9] Xu Yaming,Shi Juan,An Dongdong.Change Detection Based on Segmentation and Matched Features Point for UAV Images[J].Geomatics and Information Science of Wuhan University, 2016,41(10): 1 286-1 291(徐亚明,石娟,安动动.利用影像分割和匹配特征进行无人机影像变化检测[J].武汉大学学报·信息科学版, 2016,41(10):1 286-1 291)
[10] Wang Tiejun, Ren Sisi, Xu Ming. Research on Hybrid Object Classification Method Based on eCognition[J].Bulletin of Surveying and Mapping,2014(3):137-138(王铁军,任思思,徐明.基于eCognition的混合对象分类方法研究[J].测绘通报,2014(3):137-138)
[11] Comaniciu D,Meer P.Mean Shift:A Roubust Approach Toward Feature Space Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619
[12] Cai Jingquan. Cultural Heritage[M].Wuhan:Central China Normal University,2014 (蔡靖泉.文化遗产学[M].武汉:华中师范大学出版社,2014)
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