多共性特征联合的Landsat 8 OLI遥感影像光伏电站提取
|
摘要
遥感监督学习算法具有高度的样本依赖性,因遥感成像辐射偏差导致的数据不准确给监督分类带来较大的挑战,进而给资源监测与分析带来极大的应用困扰。本文针对在不同大气、辐射、光照和成像几何等条件下引起的不同时期和不同空间位置遥感图像上同一类别的分布存在差异现象,提出了一种多共性特征联合的Landsat 8 OLI遥感影像光伏电站提取方法。在分析光伏电站光谱不确定性(数据偏移和波形变异)规律的基础上,尝试将变换后的光谱特征、波形、纹理和波段比值等稳定性强的特征相结合,以期利用多特征间的互补性优势提高算法的泛化性能。首先将遥感影像的RGB波段转换为HLS格式,根据亮度维L计算FT纹理特征,同时加入色度H、饱和度S作为光谱特征,然后将光谱角和波段比值等对像元亮度值变化不敏感的特征考虑在内,以一类支持向量机(OCSVM)作为分类器。试验结果表明,该方法不仅能够有效克服光谱的亮度值差异,且对结构复杂的光伏电站有较好的提取效果。
The supervised classification algorithm highly depends on the training samples so that it faces big challenge in the monitoring and analysis of natural resources for the inaccurate data caused by remote sensing imaging radiation deviation. Normally,for the same category on land surface,there are always some spectrum differences in multi-temporal images and different spatial locations on the images for the different conditions of atmosphere,radiation,illumination and acquisition geometries. This paper presents a method of extracting photovoltaic( PV) power plants based on multi invariant feature combination using Landsat 8 OLI remote sensing imagery.Based on the analysis of spectral uncertainty of the ground objects,this method combines the transformed spectral characteristics,spectral curves,texture and band ratio to improve the generalization performance of the ground object extraction algorithm by utilizing the complementation of multiple features. Firstly,the RGB band of the remote sensing image is converted into the HLS format,and the Fourier transform texture feature is calculated according to the brightness dimension L,and the spectral angle,band ratio,hue and saturation are added as the spectral signatures,which is not sensitive to the changes in pixel brightness values. Finally,we choose One-class support vector machine( OCSVM) as a classifier. The experimental results show that our method not only can effectively overcome the differences in pixel brightness values of the objects but also has a better performance on extracting the complex PV power plants.
The supervised classification algorithm highly depends on the training samples so that it faces big challenge in the monitoring and analysis of natural resources for the inaccurate data caused by remote sensing imaging radiation deviation. Normally,for the same category on land surface,there are always some spectrum differences in multi-temporal images and different spatial locations on the images for the different conditions of atmosphere,radiation,illumination and acquisition geometries. This paper presents a method of extracting photovoltaic( PV) power plants based on multi invariant feature combination using Landsat 8 OLI remote sensing imagery.Based on the analysis of spectral uncertainty of the ground objects,this method combines the transformed spectral characteristics,spectral curves,texture and band ratio to improve the generalization performance of the ground object extraction algorithm by utilizing the complementation of multiple features. Firstly,the RGB band of the remote sensing image is converted into the HLS format,and the Fourier transform texture feature is calculated according to the brightness dimension L,and the spectral angle,band ratio,hue and saturation are added as the spectral signatures,which is not sensitive to the changes in pixel brightness values. Finally,we choose One-class support vector machine( OCSVM) as a classifier. The experimental results show that our method not only can effectively overcome the differences in pixel brightness values of the objects but also has a better performance on extracting the complex PV power plants.
引文
[1] LI Y,NIE D,ZHAO X,et al. Market Structure and Performance:An Empirical Study of the Chinese Solar Cell Industry[J]. Renewable and Sustainable Energy Reviews,2017(70):78-82.
[2]张乾,辛晓洲,张海龙,等.基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析[J].地球信息科学学报,2018,20(1):119-127.
[3]王胜利,张连蓬,李行,等.面向特定目标地物提取的改进最佳波段组合方法研究[J].测绘通报,2017(7):49-54.
[4]杜培军,夏俊士,薛朝辉,等.高光谱遥感影像分类研究进展[J].遥感学报,2016,20(2):236-256.
[5] SAMAT A,PERSELLO C,GAMBA P,et al. Supervised and Semi-supervised Multi-view Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification[J].Remote Sensing,2017,9(12):337.
[6] SALGADO C,ZORTEA M,SCHARCANSKI J. Classifynormalize-classify[J]. Earth Science Informatics,2017:1-21.DOI:10.1007/s12145-017-0318-2.
[7] ZHU L,XIAO P,FENG X,et al. Support Vector Machinebased Decision Tree for Snow Cover Extraction in Mountain Areas Using High Spatial Resolution Remote Sensing Image[J]. Journal of Applied Remote Sensing,2014,8(1):396-403.
[8] SHEN H,HUANG L,ZHANG L,et al. Long-term and Fine-scale Satellite Monitoring of the Urban Heat Island Effect by the Fusion of Multi-temporal and Multi-sensor Remote Sensed Data:A 26-year Case Study of the City of Wuhan in China[J]. Remote Sensing of Environment,2016(172):109-125.
[9]巫兆聪,胡忠文,张谦,等.结合光谱、纹理与形状结构信息的遥感影像分割方法[J].测绘学报,2013,42(1):44-50.
[10] HUANG X,ZHANG L. An SVM Ensemble Approach Combining Spectral,Structural,and Semantic Features for the Classification of High-resolution Remotely Sensed Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(1):257-272.
[11] ZHANG L,ZHANG L,TAO D,et al. On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(3):879-893.
[12] LI Z,SHEN H,LI H,et al. Multi-feature Combined Cloud and Cloud Shadow Detection in Gao Fen-1 Wide Field of View Imagery[J]. Remote Sensing of Environment,2017(191):342-358.
[13]陈伟,余旭初,张立福,等.采用非正定OCSVM的高光谱影像地物检测[J].遥感学报,2012,16(6):1157-1172.
[14]李厚强,刘政凯,林峰.基于分形理论的航空图像分类方法[J].遥感学报,2001,5(5):353-357.
[15]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.
[16]吕凤华,舒宁,龚龑,等.利用多特征进行航空影像建筑物提取[J].武汉大学学报(信息科学版),2017,43(5):656-660.
[17]岑奕,张艮中,张立福,等.地物光谱不确定性及光谱角制图算法适用性研究[J].光谱学与光谱分析,2015,35(10):2841-2845.
[18]王焕萍,刘勇.基于窗口傅里叶变换功率谱分析的盐田地区高分辨率遥感影像分割分类方法探讨[J].遥感技术与应用,2011,26(2):233-238.
[19] FAUVEL M,BENEDIKTSSON J A,CHANUSSOT J,et al. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(11):3804-3814.
[20]于浩,刘志红,张晓萍,等.基于傅里叶变换的梯田纹理特征提取[J].国土资源遥感,2008,20(2):39-42.
[21]王珂,肖鹏峰,冯学智,等.基于频域滤波的高分辨率遥感图像城市河道信息提取[J].遥感学报,2013,17(2):269-285.
[22] CHUN Y D,KIM N C,JANG I H. Content-based Image Retrieval Using Multiresolution Color and Texture Features[J]. IEEE Transactions on Multimedia,2008,10(6):1073-1084.
[23]周烽,封举富,石青云.一种新的基于局部傅里叶级数的纹理描述子[J].中国图象图形学报,2001,6(10):51-55.
[24]张志龙,李吉成,沈振康.局部傅里叶变换系数各阶矩的纹理鉴别性能分析[J].中国图象图形学报,2006,11(1):33-40.
[2]张乾,辛晓洲,张海龙,等.基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析[J].地球信息科学学报,2018,20(1):119-127.
[3]王胜利,张连蓬,李行,等.面向特定目标地物提取的改进最佳波段组合方法研究[J].测绘通报,2017(7):49-54.
[4]杜培军,夏俊士,薛朝辉,等.高光谱遥感影像分类研究进展[J].遥感学报,2016,20(2):236-256.
[5] SAMAT A,PERSELLO C,GAMBA P,et al. Supervised and Semi-supervised Multi-view Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification[J].Remote Sensing,2017,9(12):337.
[6] SALGADO C,ZORTEA M,SCHARCANSKI J. Classifynormalize-classify[J]. Earth Science Informatics,2017:1-21.DOI:10.1007/s12145-017-0318-2.
[7] ZHU L,XIAO P,FENG X,et al. Support Vector Machinebased Decision Tree for Snow Cover Extraction in Mountain Areas Using High Spatial Resolution Remote Sensing Image[J]. Journal of Applied Remote Sensing,2014,8(1):396-403.
[8] SHEN H,HUANG L,ZHANG L,et al. Long-term and Fine-scale Satellite Monitoring of the Urban Heat Island Effect by the Fusion of Multi-temporal and Multi-sensor Remote Sensed Data:A 26-year Case Study of the City of Wuhan in China[J]. Remote Sensing of Environment,2016(172):109-125.
[9]巫兆聪,胡忠文,张谦,等.结合光谱、纹理与形状结构信息的遥感影像分割方法[J].测绘学报,2013,42(1):44-50.
[10] HUANG X,ZHANG L. An SVM Ensemble Approach Combining Spectral,Structural,and Semantic Features for the Classification of High-resolution Remotely Sensed Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(1):257-272.
[11] ZHANG L,ZHANG L,TAO D,et al. On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(3):879-893.
[12] LI Z,SHEN H,LI H,et al. Multi-feature Combined Cloud and Cloud Shadow Detection in Gao Fen-1 Wide Field of View Imagery[J]. Remote Sensing of Environment,2017(191):342-358.
[13]陈伟,余旭初,张立福,等.采用非正定OCSVM的高光谱影像地物检测[J].遥感学报,2012,16(6):1157-1172.
[14]李厚强,刘政凯,林峰.基于分形理论的航空图像分类方法[J].遥感学报,2001,5(5):353-357.
[15]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.
[16]吕凤华,舒宁,龚龑,等.利用多特征进行航空影像建筑物提取[J].武汉大学学报(信息科学版),2017,43(5):656-660.
[17]岑奕,张艮中,张立福,等.地物光谱不确定性及光谱角制图算法适用性研究[J].光谱学与光谱分析,2015,35(10):2841-2845.
[18]王焕萍,刘勇.基于窗口傅里叶变换功率谱分析的盐田地区高分辨率遥感影像分割分类方法探讨[J].遥感技术与应用,2011,26(2):233-238.
[19] FAUVEL M,BENEDIKTSSON J A,CHANUSSOT J,et al. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(11):3804-3814.
[20]于浩,刘志红,张晓萍,等.基于傅里叶变换的梯田纹理特征提取[J].国土资源遥感,2008,20(2):39-42.
[21]王珂,肖鹏峰,冯学智,等.基于频域滤波的高分辨率遥感图像城市河道信息提取[J].遥感学报,2013,17(2):269-285.
[22] CHUN Y D,KIM N C,JANG I H. Content-based Image Retrieval Using Multiresolution Color and Texture Features[J]. IEEE Transactions on Multimedia,2008,10(6):1073-1084.
[23]周烽,封举富,石青云.一种新的基于局部傅里叶级数的纹理描述子[J].中国图象图形学报,2001,6(10):51-55.
[24]张志龙,李吉成,沈振康.局部傅里叶变换系数各阶矩的纹理鉴别性能分析[J].中国图象图形学报,2006,11(1):33-40.