一种利用高光谱像元分解技术提取水体边界的方法
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摘要
针对水体边界混合像元导致的精度损失问题,提出了一种基于高光谱混合像元分解的水体边界提取方法。该方法结合高光谱影像水体边界混合像元特有的光谱特征,削弱诸多因素对水体边界像元识别的影响,获取水体边界混合像元,降低了混合像元分解的计算量。通过混合像元的高精度分解及水体边界像元分割,进一步逼近水体的真实边界,能显著提高水体边界界定的精度。试验结果表明:用该方法进行水体提取,精度明显优于水体指数法,略优于支持向量机法,总体精度为93.86%,Kappa系数为0.87。
A water boundary extraction method based on hyper spectral mixed pixel decomposition is proposed in this paper to solve the problem of precision loss caused by water boundary mixed pixel. The method which is combined with the spectral characteristics of the hyper spectral images obtains the mixed pixels of the water boundary with weakening the impact of many factors on the pixel boundary recognition to reduce the computation amount of the mixed pixel decomposition. The precision of the boundary definition of water body can be significantly improved by approaching the true boundary of the water body with the high precision decomposition of mixed pixel and the segmentation of water boundary pixel. The experimental results show that the accuracy of the method is better than that of the water index method,which is slightly better than the support vector machine. The overall accuracy of this method is 93.86%,and the Kappa coefficient is 0.87.
A water boundary extraction method based on hyper spectral mixed pixel decomposition is proposed in this paper to solve the problem of precision loss caused by water boundary mixed pixel. The method which is combined with the spectral characteristics of the hyper spectral images obtains the mixed pixels of the water boundary with weakening the impact of many factors on the pixel boundary recognition to reduce the computation amount of the mixed pixel decomposition. The precision of the boundary definition of water body can be significantly improved by approaching the true boundary of the water body with the high precision decomposition of mixed pixel and the segmentation of water boundary pixel. The experimental results show that the accuracy of the method is better than that of the water index method,which is slightly better than the support vector machine. The overall accuracy of this method is 93.86%,and the Kappa coefficient is 0.87.
引文
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[15]PAUL A,TRIPATHI D,DUTTA D.Application and comparison of advanced supervised classifiers in extraction of water bodies from remote sensing images[J].Sustainable Water Resources Management,2017(1):1-15.
[2]陈文倩,丁建丽,李艳华,等.基于国产GF-1遥感影像的水体提取方法[J].资源科学,2015,37(6):1166-1172.
[3]PANIDI E,TSEPELEV V.NDWI-based technique for detection of change dates of the growing seasons in russian subarctic[J].2017,XLII-3/W2:179-182.DOI:10.5194/isprs-archives-XLII-3-W2-179-2017.
[4]KAPLAN G,AVDAN U.Object-based water body extraction model using Sentinel-2 satellite imagery[J].European Journal of Remote Sensing,2017,50(1):137-143.
[5]孔美美.基于混合像元分解的水体提取及变化监测研究[D].泉州:华侨大学,2016.
[6]黄勇,张文建,郁凡.基于图像熵的卫星云图分类方法[J].大气科学学报,2012,35(5):633-639.
[7]安丽.基于Hough变换的高分辨率遥感影像道路提取[D].南昌:东华理工大学,2015.
[8]童李霞,燕琴,骆成凤,等.基于NDWI分割与面向对象的水体信息提取[J].地理空间信息,2017,15(5):57-59,11.
[9]李艳华,丁建丽,闫人华.基于国产GF-1遥感影像的山区细小水体提取方法研究[J].资源科学,2015,37(2):408-416.
[10]王瑾杰,丁建丽,张成,等.基于GF-1卫星影像的改进SWI水体提取方法[J].国土资源遥感,2017,29(1):29-35.
[11]崔媛.高光谱图像混合像元分解技术研究[D].沈阳:沈阳航空航天大学,2016.
[12]YANG C,WANG J,LIQUAN Q U,et al.Research on the extraction of surface feature abundance based on the least square mixed pixel decomposition[J].Science of Surveying&Mapping,2017,42(9):143-150,157.
[13]段金亮,王杰,文星跃.基于端元变化的两种混合像元分解算法比较研究[J].资源开发与市场,2017,33(6):651-655,640.
[14]王毓乾.基于空间-光谱分析的高光谱遥感影像稀疏解混研究[J].测绘学报,2017,46(8):1072.
[15]PAUL A,TRIPATHI D,DUTTA D.Application and comparison of advanced supervised classifiers in extraction of water bodies from remote sensing images[J].Sustainable Water Resources Management,2017(1):1-15.