光谱解混技术的研究及其在油量分析中的应用
摘要
高光谱遥感技术以其海量光谱信息在地质勘探、军事应用、植被检测、海洋遥感等领域发挥着巨大作用。但是由于其空间分辨率的限制和各类地物的复杂多样性,图像中除了纯像元之外,还有可能存在包含多种地物的混合像元,因而不能达到高精度的遥感应用目的。
光谱解混技术就是利用高光谱丰富的波段信息从像元级进入亚像元级,分解出混合像元中的基本组成单元,即端元,并求出这些端元所占的比例。通过光谱解混的预处理,地物分类识别更为精确,因此在矿物质预测、城市布局分析、化学成分检测等方面有着广阔的发展空间。本论文意图在海洋溢油检测的研究中利用光谱解混技术,实现对高光谱溢油图像中油和水含量的分析,提高溢油检测的精度。
论文基于高光谱混合像元的形成机理,在线性光谱混合模型的基础上,利用端元分布的空间相关性,端元光谱的非负性和连续性约束,对光谱解混技术精确性进行提高。研究内容主要包括以下几方面：
(1)分析高光谱解混的流程以及多种端元提取的方法,提出利用已有的N-FINDR端元选择算法对高光谱溢油图像进行处理,利用端元之间的相关性对N-FINDR进行改进,实现端元的准确提取并且减少计算量。
(2)模拟生成混合像元的影像,验证改进的N-FINDR算法有效性。利用真实水和汽油的光谱曲线模拟高光谱混合图像,并用改进的N-FINDR算法提取端元,通过光谱分解确定每个像元中各端元的比例,从而分析影像中溢油的含量信息。
(3)选用实际中的高光谱溢油图像,进行前期图像预处理以及主成分降维处理,进而利用改进的N-FINDR算法实现混合像元的分解,并用于实际图像的油量分析。
对实验生成的模拟数据和实际测试数据的处理结果都验证了论文所提出算法的有效性,论文的研究内容也说明了利用光谱解混所得的端元丰度进行油量分析是切实可行的。
With the mass spectral information, hyperspectral remote sensing technology plays a huge role in geological exploration, military applications, detection of vegetation, ocean remote sensing and other areas. However, due to the limitations of spatial resolution and the complexity and diversity of the various types of surface features, images, in addition to pure pixel, there may exist a mixture that contains a variety of surface features pixel, and thus can not achieve the accuracy of remote sensing applications.
Spectral unmixing technique is to use the high spectral band information from the pixel level to enter the sub-pixel level, to decompose the basic unit-endmember of the mixed pixel, and to calculate the proportion of these endmembers. By the pretreatment of spectral unmixing, object classification and recognition is more accurate, so there is a broad space for mineral prediction, urban layout analysis, chemical composition and detection. This article attempts in the study of marine oil spill detection using spectral unmixing techniques, to analyze oil and water content of the hyperspectral oil spill image, to improve the accuracy of oil spill detection.
Paper based on the mechanism of hyperspectral mixed pixel and linear spectral mixing model, using endmember distribution spatial correlation and endmember spectra of non-negative and continuity constraints, to improve the spectral unmixing technical accuracy. Researches include the following aspects:
Firstly, analysising of hyperspectral unmixing process and a variety of endmember extraction methods, proposing the existing N-FINDR endmember selection algorithm to process the hyperspectral oil spill image, and using the correlation between endmembers to improve the N-FINDR and to achieve accurate extraction of endmembers and reduce computation.
Secondly, create mixed-pixel image to verify the improved N-FINDR. Use the spectral curves of real water and gasoline to produce the mixed image of the hyperspectral, and then determining the proportion of each endmember by spectral decomposition with the improved N-FINDR. In the end, get the content of oil in the image.
At last, select the actual hyperspectral oil spill image, for image preprocessing as well as the main ingredient to reduce the dimension. And then use the improved N-FINDR to achieve mixed pixel decomposition, and used for the oil analysis of the actual image.
The results of simulated data and actual test data verified the algorithm proposed by this paper is effective. The research of this paper also shows that the using of spectral unmixing to get the endmembers'content is feasible for the using of oil analysis.
光谱解混技术就是利用高光谱丰富的波段信息从像元级进入亚像元级,分解出混合像元中的基本组成单元,即端元,并求出这些端元所占的比例。通过光谱解混的预处理,地物分类识别更为精确,因此在矿物质预测、城市布局分析、化学成分检测等方面有着广阔的发展空间。本论文意图在海洋溢油检测的研究中利用光谱解混技术,实现对高光谱溢油图像中油和水含量的分析,提高溢油检测的精度。
论文基于高光谱混合像元的形成机理,在线性光谱混合模型的基础上,利用端元分布的空间相关性,端元光谱的非负性和连续性约束,对光谱解混技术精确性进行提高。研究内容主要包括以下几方面：
(1)分析高光谱解混的流程以及多种端元提取的方法,提出利用已有的N-FINDR端元选择算法对高光谱溢油图像进行处理,利用端元之间的相关性对N-FINDR进行改进,实现端元的准确提取并且减少计算量。
(2)模拟生成混合像元的影像,验证改进的N-FINDR算法有效性。利用真实水和汽油的光谱曲线模拟高光谱混合图像,并用改进的N-FINDR算法提取端元,通过光谱分解确定每个像元中各端元的比例,从而分析影像中溢油的含量信息。
(3)选用实际中的高光谱溢油图像,进行前期图像预处理以及主成分降维处理,进而利用改进的N-FINDR算法实现混合像元的分解,并用于实际图像的油量分析。
对实验生成的模拟数据和实际测试数据的处理结果都验证了论文所提出算法的有效性,论文的研究内容也说明了利用光谱解混所得的端元丰度进行油量分析是切实可行的。
With the mass spectral information, hyperspectral remote sensing technology plays a huge role in geological exploration, military applications, detection of vegetation, ocean remote sensing and other areas. However, due to the limitations of spatial resolution and the complexity and diversity of the various types of surface features, images, in addition to pure pixel, there may exist a mixture that contains a variety of surface features pixel, and thus can not achieve the accuracy of remote sensing applications.
Spectral unmixing technique is to use the high spectral band information from the pixel level to enter the sub-pixel level, to decompose the basic unit-endmember of the mixed pixel, and to calculate the proportion of these endmembers. By the pretreatment of spectral unmixing, object classification and recognition is more accurate, so there is a broad space for mineral prediction, urban layout analysis, chemical composition and detection. This article attempts in the study of marine oil spill detection using spectral unmixing techniques, to analyze oil and water content of the hyperspectral oil spill image, to improve the accuracy of oil spill detection.
Paper based on the mechanism of hyperspectral mixed pixel and linear spectral mixing model, using endmember distribution spatial correlation and endmember spectra of non-negative and continuity constraints, to improve the spectral unmixing technical accuracy. Researches include the following aspects:
Firstly, analysising of hyperspectral unmixing process and a variety of endmember extraction methods, proposing the existing N-FINDR endmember selection algorithm to process the hyperspectral oil spill image, and using the correlation between endmembers to improve the N-FINDR and to achieve accurate extraction of endmembers and reduce computation.
Secondly, create mixed-pixel image to verify the improved N-FINDR. Use the spectral curves of real water and gasoline to produce the mixed image of the hyperspectral, and then determining the proportion of each endmember by spectral decomposition with the improved N-FINDR. In the end, get the content of oil in the image.
At last, select the actual hyperspectral oil spill image, for image preprocessing as well as the main ingredient to reduce the dimension. And then use the improved N-FINDR to achieve mixed pixel decomposition, and used for the oil analysis of the actual image.
The results of simulated data and actual test data verified the algorithm proposed by this paper is effective. The research of this paper also shows that the using of spectral unmixing to get the endmembers'content is feasible for the using of oil analysis.
引文
[1]薛绮.基于线性混合模型的高光谱图像端元提取方法研究[D].国防科学技术大学研究生院,2004.11.
[2]杨倩倩.高光谱溢油图像特征提取在油种识别中的应用[D].大连海事大学,2010.
[3]臧影.高光谱溢油图像波段选择在油膜厚度估算中的应用[D].大连海事大学,2010.
[4]包海燕.高光谱溢油图像分类算法研究[D].大连海事大学,2011.
[5]徐长健.基于小波变换的高光谱溢油图像压缩方法的研究[D].大连海事大学,2011.
[6]童庆禧,张兵,郑兰芬.高光谱遥感--原理、技术与应用[M].北京：高等教育出版社,2006.6.
[7]J W Boardman, F A Kruse, R 0 Green. Mapping target signatures via partial unmixing of AVIRIS data[C].In Proceedings Summaries JPL Airborne Earth Science Workshop,Pasadena, CA,1995:23-26.
[8]M E Winter. N-FINDR:An Algorithm for Fast Autonomous Spectral Endmember Determination in HyperSpectral Data[C]. In Proceedings of SPIE:Imaging Spectromety,1999:266-275.
[9]R A Neville, K Staenz, T Szeredi,J Lefebvre P. Hauff. Automatic endmemeber extraction from hyperspectral data for mineral exploration[C].In Proceedings 4th International Airborne Remote Sensing Conference and Exhibition/21st Canada Symposia Remote Sensing, Ottawa, Canada, Jun.1999:21-24.
[10]J M P. Nascimento,J M B Dias. Vertex Component Analysis:A Fast Algorithm to Unmix Hyperspectral Data[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):898-910.
[11]Craig M D.1994. Minimum volume transforms for remotely sensed data. IEEE Transactions on Geoscience and Remote Sensing,32:542-552.
[12]Bateson C A, Asner G P, Wessman C A.2000. Endmenmber bundles:A New Approach to incorporating endmember variability into spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing,38(2):1083-1094
[13]Plaza A, Martinez P, Perez R M.2002. Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remote Sensing,40:9-20.
[14]J C Harsanyi, C I Chang. Hyperspectral image classification and dimensionality reduction:Anorthogonal subspace projection[J]. IEEE Transactions on Geoscience and Remote Sensing,1994,32(4):779-785.
[15]贾森.非监督的高光谱图像解混技术研究[D].浙江大学,2007.12.
[16]吴波,张良培,李平湘.非监督正交子空间投影的高光谱混合像元自动分解[J].中国图象图形学报,2004,9(11)：1392-1396.
[17]D C Heinz, C I Chang. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing, Mar.2001,39(3):529-545.
[18]Lennon M, Mercierg, Mouchot M C, et al,2001. Spectral unmixing of hyperspectral image with the independent component analysis and wavelet packets. IGARSS 2001. Conference.
[19]Kruskal J B.1969. Toward a practical method which helps uncover the structureof a set of multivariate observations by finding the linear transformation which optimizes a newindex of condensation, Statistical Computation. New York:Academic Pree.
[20]齐建成,朱述龙,朱宝山等.基于端元独立性的端元数目自动获取方法[J].测绘科学,2009,34(6)：206-208.
[21]赵春晖,齐滨,王玉磊.一种改进的N-FINDR高光谱端元提取算法[J].电子与信息学报,2012,34(2)：499-503.
[22]刘恒殊,彭风华,黄廉卿.超光谱遥感图像特征分析[J].光学精密工程,2001,9(4)：92-235
[23]Robert A. Schowengerdt遥感图像处理模型与方法[M].北京：电子工业出版社,2010.
[24]李玲.遥感数字图像处理[M].重庆：重庆大学出版社,2010.1.
[25]朱述龙,朱宝山,王红卫.遥感图像处理与应用[M].北京：科学出版社,2006.
[26]John A. Richards, Xiuping Jia遥感数字图像分析[M].北京：电子工业出版社,2009.
[27]赵春晖,刘春红.超谱遥感图像降维方法研究现状与分析[J].中国空间科学技术,2004,5(10)：28-36
[28]杨诸胜,郭雷,罗欣,胡新韬.一种基于主成分分析的高光谱图像波段选择算法[J].微电子学与计算机,2006,23(12)：72-74.
[29]张绍荣,苏令华.一种基于主成分分析的高光谱图像压缩方法[J].无线电工程,2005,35(9)：53-54.
[30]田野,赵春晖,季亚新.主成分分析在高光谱遥感图像降维中的应用[J].哈尔滨师范大学自然科学学报,2007,23(5)：58-60.
[31]陈树.聚类算法模型的研究及应用[D].无锡：江南大学,2007.
[32]李小娟,刘晓萌等.ENVI遥感影像处理教程[M].北京：中国环境科学出版社,2008.
[33]张晔,张钧萍.遥感超谱(Hyperspectral)图像处理技术[J].中国图像图形学报,2001,6(1)：6-13.
[34]Sen Jia,Zhen Ji,Yuntao Qian. Band selection based hyperspectral unmixing. Imaging Systems and Techniques,2009.IST'09. IEEE International Workshop on[J].2009:303-306.
[2]杨倩倩.高光谱溢油图像特征提取在油种识别中的应用[D].大连海事大学,2010.
[3]臧影.高光谱溢油图像波段选择在油膜厚度估算中的应用[D].大连海事大学,2010.
[4]包海燕.高光谱溢油图像分类算法研究[D].大连海事大学,2011.
[5]徐长健.基于小波变换的高光谱溢油图像压缩方法的研究[D].大连海事大学,2011.
[6]童庆禧,张兵,郑兰芬.高光谱遥感--原理、技术与应用[M].北京：高等教育出版社,2006.6.
[7]J W Boardman, F A Kruse, R 0 Green. Mapping target signatures via partial unmixing of AVIRIS data[C].In Proceedings Summaries JPL Airborne Earth Science Workshop,Pasadena, CA,1995:23-26.
[8]M E Winter. N-FINDR:An Algorithm for Fast Autonomous Spectral Endmember Determination in HyperSpectral Data[C]. In Proceedings of SPIE:Imaging Spectromety,1999:266-275.
[9]R A Neville, K Staenz, T Szeredi,J Lefebvre P. Hauff. Automatic endmemeber extraction from hyperspectral data for mineral exploration[C].In Proceedings 4th International Airborne Remote Sensing Conference and Exhibition/21st Canada Symposia Remote Sensing, Ottawa, Canada, Jun.1999:21-24.
[10]J M P. Nascimento,J M B Dias. Vertex Component Analysis:A Fast Algorithm to Unmix Hyperspectral Data[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):898-910.
[11]Craig M D.1994. Minimum volume transforms for remotely sensed data. IEEE Transactions on Geoscience and Remote Sensing,32:542-552.
[12]Bateson C A, Asner G P, Wessman C A.2000. Endmenmber bundles:A New Approach to incorporating endmember variability into spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing,38(2):1083-1094
[13]Plaza A, Martinez P, Perez R M.2002. Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remote Sensing,40:9-20.
[14]J C Harsanyi, C I Chang. Hyperspectral image classification and dimensionality reduction:Anorthogonal subspace projection[J]. IEEE Transactions on Geoscience and Remote Sensing,1994,32(4):779-785.
[15]贾森.非监督的高光谱图像解混技术研究[D].浙江大学,2007.12.
[16]吴波,张良培,李平湘.非监督正交子空间投影的高光谱混合像元自动分解[J].中国图象图形学报,2004,9(11)：1392-1396.
[17]D C Heinz, C I Chang. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing, Mar.2001,39(3):529-545.
[18]Lennon M, Mercierg, Mouchot M C, et al,2001. Spectral unmixing of hyperspectral image with the independent component analysis and wavelet packets. IGARSS 2001. Conference.
[19]Kruskal J B.1969. Toward a practical method which helps uncover the structureof a set of multivariate observations by finding the linear transformation which optimizes a newindex of condensation, Statistical Computation. New York:Academic Pree.
[20]齐建成,朱述龙,朱宝山等.基于端元独立性的端元数目自动获取方法[J].测绘科学,2009,34(6)：206-208.
[21]赵春晖,齐滨,王玉磊.一种改进的N-FINDR高光谱端元提取算法[J].电子与信息学报,2012,34(2)：499-503.
[22]刘恒殊,彭风华,黄廉卿.超光谱遥感图像特征分析[J].光学精密工程,2001,9(4)：92-235
[23]Robert A. Schowengerdt遥感图像处理模型与方法[M].北京：电子工业出版社,2010.
[24]李玲.遥感数字图像处理[M].重庆：重庆大学出版社,2010.1.
[25]朱述龙,朱宝山,王红卫.遥感图像处理与应用[M].北京：科学出版社,2006.
[26]John A. Richards, Xiuping Jia遥感数字图像分析[M].北京：电子工业出版社,2009.
[27]赵春晖,刘春红.超谱遥感图像降维方法研究现状与分析[J].中国空间科学技术,2004,5(10)：28-36
[28]杨诸胜,郭雷,罗欣,胡新韬.一种基于主成分分析的高光谱图像波段选择算法[J].微电子学与计算机,2006,23(12)：72-74.
[29]张绍荣,苏令华.一种基于主成分分析的高光谱图像压缩方法[J].无线电工程,2005,35(9)：53-54.
[30]田野,赵春晖,季亚新.主成分分析在高光谱遥感图像降维中的应用[J].哈尔滨师范大学自然科学学报,2007,23(5)：58-60.
[31]陈树.聚类算法模型的研究及应用[D].无锡：江南大学,2007.
[32]李小娟,刘晓萌等.ENVI遥感影像处理教程[M].北京：中国环境科学出版社,2008.
[33]张晔,张钧萍.遥感超谱(Hyperspectral)图像处理技术[J].中国图像图形学报,2001,6(1)：6-13.
[34]Sen Jia,Zhen Ji,Yuntao Qian. Band selection based hyperspectral unmixing. Imaging Systems and Techniques,2009.IST'09. IEEE International Workshop on[J].2009:303-306.