基于SIFT特征与SVM分类的地震灾情图像信息异常检测方法
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摘要
通过分析以往震后获取的图像信息发现,部分信息存在与地震发生时间不吻合、不属于地震影响范围或与地震灾情无关等异常。通过将图像分类算法运用到震后灾情图像信息的异常检测中,提出了一种基于SIFT特征与SVM分类的地震灾情图像信息异常检测模型,以2013年芦山7.0级地震建筑物破坏灾情图像为例对模型进行验证。结果表明:该模型对图像信息异常的检测效果较好,可进一步补充和完善地震应急救援的灾情信息源,为政府抗震救灾科学决策提供灾情信息支撑。
Through analyzing the image information obtained after the earthquake,we found that some of the information does not match the time of the earthquake,does not belong to the earthquake influence field or has nothing to do with the earthquake disaster. In this paper,we applied the image classification algorithm to the detection of post-earthquake disaster image information anomaly,and proposed an earthquake disaster image information anomaly detection model based on SIFT feature and SVM classification,and verified the model by taking the damage image of the Lushan M7.0 earthquake as an example. The results show that the algorithm model has better detection effect on image information anomaly,it can further supply and improve the disaster information source for earthquake emergency rescue,and provide disaster information support for the government's scientific decision-making for earthquake relief.
Through analyzing the image information obtained after the earthquake,we found that some of the information does not match the time of the earthquake,does not belong to the earthquake influence field or has nothing to do with the earthquake disaster. In this paper,we applied the image classification algorithm to the detection of post-earthquake disaster image information anomaly,and proposed an earthquake disaster image information anomaly detection model based on SIFT feature and SVM classification,and verified the model by taking the damage image of the Lushan M7.0 earthquake as an example. The results show that the algorithm model has better detection effect on image information anomaly,it can further supply and improve the disaster information source for earthquake emergency rescue,and provide disaster information support for the government's scientific decision-making for earthquake relief.
引文
曹彦波,吴艳梅,许瑞杰,等.2017.基于微博舆情数据的震后有感范围提取[J].地震研究,40(2):303-310.
曹彦波.2018.基于新浪微博的2018年云南通海5.0级地震舆情时空特征分析[J].地震研究,41(4):525-533.
崔红艳,曹建芳.2016.基于改进的分布式K-Means特征聚类的海量场景图像检索[J].计算机应用与软件,33(6):195-199.
傅卫平,秦川,刘佳等.2011.基于SIFT算法的图像目标匹配与定位[J].仪器仪表学报,32(1):163-169.
胡素平,帅向华.2017.网络地震灾情信息智能处理模型与地震烈度判定方法研究[J].震灾防御技术,7(4):420-430.
李红丽,许春香,马耀锋.2018.基于多核学习SVM的图像分类识别算法[J].现代电子技术,41(6):50-52.
刘文.2017.地震灾情信息采集与分类分析[J].科学与信息化,(29):5-6.
任恒怡,贺松,陈文亮.2017.一种改进的K-means聚类算法在图像分割中的应用[J].通信技术,50(12):2704-2707.
汤海林.2013.基于SIFT的图像特征提取算法研究[J].电脑开发与应用,21(8):54-56.
完文韬,杨成禹.2018.改进的SIFT算法在图像特征点匹配中的应用[J].长春理工大学学报(自然科学版),41(1):44-47.
汪松.2013.基于SIFT算法的图像匹配方法研究[D].西安:西安电子科技大学.
杨松,邵龙潭,宋维波,等.2016.一种基于SIFT特征的快速图像匹配算法[J].计算机应用与软件,33(7):186-189.
曹彦波.2018.基于新浪微博的2018年云南通海5.0级地震舆情时空特征分析[J].地震研究,41(4):525-533.
崔红艳,曹建芳.2016.基于改进的分布式K-Means特征聚类的海量场景图像检索[J].计算机应用与软件,33(6):195-199.
傅卫平,秦川,刘佳等.2011.基于SIFT算法的图像目标匹配与定位[J].仪器仪表学报,32(1):163-169.
胡素平,帅向华.2017.网络地震灾情信息智能处理模型与地震烈度判定方法研究[J].震灾防御技术,7(4):420-430.
李红丽,许春香,马耀锋.2018.基于多核学习SVM的图像分类识别算法[J].现代电子技术,41(6):50-52.
刘文.2017.地震灾情信息采集与分类分析[J].科学与信息化,(29):5-6.
任恒怡,贺松,陈文亮.2017.一种改进的K-means聚类算法在图像分割中的应用[J].通信技术,50(12):2704-2707.
汤海林.2013.基于SIFT的图像特征提取算法研究[J].电脑开发与应用,21(8):54-56.
完文韬,杨成禹.2018.改进的SIFT算法在图像特征点匹配中的应用[J].长春理工大学学报(自然科学版),41(1):44-47.
汪松.2013.基于SIFT算法的图像匹配方法研究[D].西安:西安电子科技大学.
杨松,邵龙潭,宋维波,等.2016.一种基于SIFT特征的快速图像匹配算法[J].计算机应用与软件,33(7):186-189.