结合mRMR选择和IFCM聚类的遥感影像分类算法
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摘要
为解决高分影像特征间相关性大冗余度高、FCM聚类稳健性差带来的分类精度不佳问题,提出一种基于mRMR选择和改进FCM聚类的影像分类算法。首先基于对象置信度指标(OC)进行影像分割,然后利用mRMR算法实现特征选择,解决特征冗余问题,最后将提取的特征输入分类器通过IFCM聚类,得到最终分类结果。试验结果表明,本文算法能减少特征间相关性,降低冗余,并有效提高影像分类精度。
In order to solve the problem of poor classification precision caused by high correlation redundancy between features of highresolution image and poor robustness of FCM clustering,an image classification algorithm based on mRMR selection and IFCM clustering is proposed. First,the image segmentation is carried out based on the object confidence index( OC),then the feature selection is realized by mRMR algorithm to solve the feature redundancy problem. The extracted feature is put in classifier and final classification result is clustered by IFCM algorithm. Comparison of experimental results show that the proposed algorithm can reduce feature correlation and redundancy and effectively improve image classification accuracy.
In order to solve the problem of poor classification precision caused by high correlation redundancy between features of highresolution image and poor robustness of FCM clustering,an image classification algorithm based on mRMR selection and IFCM clustering is proposed. First,the image segmentation is carried out based on the object confidence index( OC),then the feature selection is realized by mRMR algorithm to solve the feature redundancy problem. The extracted feature is put in classifier and final classification result is clustered by IFCM algorithm. Comparison of experimental results show that the proposed algorithm can reduce feature correlation and redundancy and effectively improve image classification accuracy.
引文
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[2]佃袁勇,杨光,方圣辉.傅里叶谱纹理和光谱信息结合的高分辨率遥感影像地表覆盖分类[J].武汉大学学报(信息科学版),2017,42(3):362-368.
[3]程诗尧,梅天灿,刘国英.顾及结构特征的多层次马尔科夫随机场模型在影像分类中的应用[J].武汉大学学报(信息科学版),2015,40(9):1180-1187.
[4]DEMIR B,BRUZZONE L.Histogram-based attribute profiles for classification of very high resolution remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(4):2096-2107.
[5]YANG M,CHANG H Y,LUO W X,et al.Fisher discrimination dictionary pair learning for image classification[J].Neurocomputing,2017,269(12):13-20.
[6]慎利,吴林梅,方灿明.面向高分辨率遥感影像分类的多尺度光谱-空间-语义特征融合[J].地理与地理信息科学,2017,33(5):1-8.
[7]陈苏婷,王慧.多尺度多特征融合的高分辨率遥感影像分类[J].量子电子学报,2016,33(4):420-426.
[8]王超,行鸿彦,熊允波,等.对象置信度指引下的高分辨率遥感影像分割[J].仪器仪表学报,2017,38(9):2282-2290.
[9]王云艳,何楚,涂峰,等.特征选择双层SVM的融合算法用于极化SAR图像分类[J].武汉大学学报(信息科学版),2015,40(9):1157-1162.
[10]LI X L,SHAO Z J,QIAN J X.An optimizing method based on autonomous animates:fish-swarm algorithm[J].Journal of Systems Engineering-theory and Practice,2002(11):32-38.
[11]SUN G,LIU Q,LIU Q,et al.A novel approach for edge detection based on the theory of universal gravity[J].Pattern Recognition,2007,40(10):2766-2775.
[12]MERTENS K C,BAETS B,VERBEKE L P C,et al.Asub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models[J].International Journal of Remote Sensing,2006,27(15):3293-3310.
[13]贾永红,冯在梅,沈庭乐.基于标记分水岭分割的高分辨率影像水体信息提取[J].测绘通报,2014(9):31-33.
[14]GONZALEZ R C,WOODS R E,EDDINS S L,等.数字图像处理[M].北京:电子工业出版社,2014.
[15]赵时英.遥感应用分析原理与方法[M].北京:科学出版社,2003.