遥感影像混合像元分解及超分辨率重建研究进展
收稿日期: 2009-06-01
修回日期: 2010-01-01
网络出版日期: 2010-06-25
基金资助
国家自然科学基金项目(40471111, 70571076); 863 计划(2006AA12Z215,2007AA12Z233); 国际科技合作项目 (2007DFC20180);中国科学院知识创新项目(KZCX2-YW-308).
Mixed-pixel Decomposition and Super-resolution Reconstruction of RS Image
Received date: 2009-06-01
Revised date: 2010-01-01
Online published: 2010-06-25
随着遥感应用的深入，传统将遥感影像像元当作纯净像元的方式所带来的问题已经被广泛认识到，混合像 元分解的相关理论和技术成为遥感领域的一个热点问题。本文总结了混合像元分解及超分辨率影像重建的主要理 论和方法。根据超分辨率影像重建的主要流程，分别回顾了混合像元端元类型选择、端元丰度分解和超分辨率影像 的重建，并对相关模型和技术给出了总结和评价。端元类型选择是确定在影像范围包含的纯净地物类型，重点介绍 了基于统计学和几何学的两种方法。端元丰度估计是目前该领域研究最多的方向之一，集中了很多新的理论和方 法，可变端元分解和盲源分解作为2 种效果较好的方法在文中作了详细的回顾和评价。空间自相关性是对丰度估 计的结果进行超分辨率重建的主要理论基础，如何在丰度约束条件下最大化空间自相关性是大多数基于混合像元 分解超分辨率重建的目标。最后，文章在总结目前混合像元分解及超分辨率遥感影像理论发展的基础上，给出了一 些意见和展望，指出考虑混合像元形成机理、综合多种模型及先验信息将有助于基于混合像元分解的超分辨率遥 感影像研究。
胡茂桂,王劲峰 . 遥感影像混合像元分解及超分辨率重建研究进展[J]. 地理科学进展, 2010 , 29(6) : 747 -756 . DOI: 10.11820/dlkxjz.2010.06.015
Remote sensing technology has been used in a wide range of applications, but the mixed-pixel phenomenon has been a persistent problem. In traditional classification, every pixel is considered a pure pixel and can be classified as only one type. This affects the accuracy and precision of results in applications. Recently, the problem has been studied by many researchers who have adopted many models and methods to decompose mixed-pixels and reconstruct super-resolution images from the low-resolution originals. In this article, we give a literature review of the development of mixed-pixel decomposition and super-resolution reconstruction. In accord with the main flow in the process, three aspects are reviewed: (1) endmember selection, (2) abundance estimation, and (3) super-resolution reconstruction. Endmember selection aims at selecting pure objects in the whole image range. Statistical methods and geometrical methods have been covered in detail for endmember selection. Abundance estimation of endmembers in pixels is another vital step attracting a great deal of research. It involves a number of new models and methods. We put an emphasis on variable endmember spectral mixture analysis and blind sources separation methods, which perform well and seem promising. Super-resolution reconstruction is based on the result of abundance estimation. How to maximize the spatial auto-correlation is the main objective when reconstructing super-resolution images. We review the most commonly used pixel-swapping method at length and discuss some problems presented in the study. Finally, some suggestions are brought forward for the mixed-pixel decomposition and super-resolution reconstruction of RS images.
[1] 赵英时. 遥感应用分析原理与方法. 北京: 科学出版社, 2003.
[2] 赖志斌, 夏曙东, 承继成. 高分辨率遥感卫星数据在城 市生态环境评价中的应用模型研究. 地理科学进展, 2000, 19(4): 359-365.
[3] 李亚云, 杨秀春, 朱晓华, 等. 遥感技术在中国土地荒漠 化监测中的应用进展. 地理科学进展, 2009, 28 (1): 55- 62.
[4] Woodcock C E, Strahler A H. The factor of scale in remote sensing. Remote Sensing of Environment, 1987, 21(3): 311-322.
[5] Roberts D A, Smith M O, Adams J B. Green vegetation, nonphotosynthetic vegetation, and soils in aviris data. Remote Sensing of Environment, 1993, 44(2-3): 255-269.
[6] Smith M O, Ustin S L, Adams J B, et al. Vegetation in deserts: I. a regional measure of abundance from multispectral images. Remote Sensing of Environment, 1990, 31(1): 1-26.
[7] Small C. Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 2001, 22(7): 1305-1334.
[8] Green A A, Berman M, Switzer P et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1): 65-74.
[9] Chang C I, Plaza A. A fast iterative algorithm for implementation of pixel purity index. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 63-67.
[10] Plaza A, Chang C I. Fast implementation of pixel purity index algorithm. SPIE Symposium on Defense and Security, XI Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery, 2005.
[11] Bateson A, Curtiss B. A method for manual endmember selection and spectral unmixing. Remote Sensing of Environment, 1996, 55(3): 229-243.
[12] Lelong C C D, Pinet P C, Poilv H. Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce(France). Remote Sensing of Environment, 1998, 66(2): 179-191.
[13] Nascimento J M P, Dias J M B. Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.
[14] Gruninger J, Ratkowski A J, Hoke M L. The sequential maximum angle convex cone (SMACC) endmember model. Proceedings SPIE, Algorithms for Multispectral and Hyper- spectral and Ultraspectral Imagery, 2004.
[15] Plaza A, Chang C I. An improved N-FINDR algorithm in implementation. SPIE Symposium on Defense and Security, XI Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery, 2005.
[16] 夏学齐, 田庆久, 杜凤兰. 高光谱遥感图像的单形体分 析方法. 中国图象图形学报, 2004(12): 1486-1491.
[17] Bastin L. Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing, 1997, 18(17): 3629-3648.
[18] Holben B N, Shimabukuro Y E. Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors. International Journal of Remote Sensing, 1993, 14(11): 2231-2240.
[19] Keshava N, Mustard J F. Spectral unmixing. Signal Processing Magazine, IEEE, 2002, 19(1): 44-57.
[20] Ray T W, Murray B C. Nonlinear spectral mixing in desert vegetation. Remote Sensing Of Environment, 1996, 55(1): 59-64.
[21] Cross A M, Settle J J, Drake N A, et al. Subpixel measurement of tropical forest cover using avhrr data. International Journal of Remote Sensing, 1991,12(5): 1119-1129.
[22] Quarmby N A, Townshend J R G, et al. Linear mixture modelling applied to AVHRR data for crop area estimation. International Journal of Remote Sensing, 1992, 13 (3): 415-425.
[23] Boardman J W. Geometric mixture analysis of imaging spectrometry data. IEEE International Geoscience and Remote sensing Symposium, 1994: 2369-2371.
[24] Foody G M. Approaches for the production and evaluation of fuzzy land cover classifications from remotely -sensed data. International Journal of Remote Sensing, 1996, 17 (7): 1317-1340.
[25] Masell F, Rodolfi A, Conese C. Fuzzy classification of spatially degraded thematic mapper data for the estimation of sub -pixel components. International Journal of Remote Sensing, 1996, 17(3): 537-551.
[26] 黄昕, 张良培, 李平湘. 基于多尺度特征融合和支持向 量机的高分辨率遥感影像分类. 遥感学报, 2007, 11(1): 48-54.
[27] Brown M, Gunn S R, Lewis H G. Support vector machines for optimal classification and spectral unmixing. Ecological Modelling, 1999, 120(2-3): 167-179.
[28] 吴波, 张良培, 李平湘. 基于支撑向量回归的高光谱混 合像元非线性分解. 遥感学报, 2006, 10(3): 312-318.
[29] Foody G M, Lucas R M, Curran P J et al. Non -linear mixture modelling without end-members using an artificial neural network. International Journal of Remote Sensing, 1997, 18(4): 937-953.
[30] Carpenter G A, Gopal S, Macomber S et al. A neural network method for mixture estimation for vegetation mapping. Remote Sensing of Environment, 1999, 70 (2): 138- 152.
[31] 张彦, 邵美珍. 基于径向基函数神经网络的混合像元分 解. 遥感学报, 2002, 6(4): 285-288.
[32] 徐宏根, 马洪超, 李德仁. 结合SOM 神经网络和混合像 元分解的高光谱影像分类方法研究. 遥感学报, 2007, 11(6): 778-786.
[33] 吴柯, 张良培, 李平湘. 一种端元变化的神经网络混合 像元分解方法. 遥感学报, 2007, 11(1): 20-26.
[34] Li L, Ustin S L, Lay M. Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh apping: a case study in China Camp, CA, USA. International Journal of Remote Sensing, 2005, 26(23): 5193-5207.
[35] Roberts D A, Gardner M, Church R, et al. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 1998, 65(3): 267-279.
[36] García -Haro F J, Sommer S, Kemper T. A new tool for variable multiple endmember spectral mixture analysis (VMESMA). International Journal of Remote Sensing, 2005, 26(10): 2135-2162.
[37] Franke J, Roberts D A, Halligan K, et al. Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 2009, 113(8): 1712-1723.
[38] Mei T, Mertins A, Yin F, et al. Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices. Signal Processing, 2008, 88(8): 1990-2007.
[39] Bijaoui A, Nuzillard D, Barma T D. Bss, classification and pixel demixing. Proceedings of the 5th International Conference on Independent Component Analysis and Blind Source Separation, 2004: 96-103.
[40] 芮挺, 王金岩, 沈春林, 等. 基于线性分析的特征不变性 目标识别. 计算机工程, 2005, 31(15): 4-6.
[41] Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13 (4- 5): 411-430.
[42] Tu T M. Unsupervised signature extraction and separation in hyperspectral images: A noise -adjusted fast independent component analysis approach. Optical Engineering, 2000, 39(4): 897-906.
[43] Zhang X, Chen C H. New independent component analysis method using higher order statistics with application to remote sensing images. Optical Engineering, 2002, 41 (7): 1717-1728.
[44] Wang J, Chang C I. Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44 (9): 2601-2616.
[45] Wang J, Chang C. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1586-1600.
[46] Attias H. Independent factor analysis. Neural Computation, 1999, 11(4): 803-851.
[47] Nascimento J M P, Dias J M B. Does independent component analysis play a role in unmixing hyperspectral data? IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(1): 175-187.
[48] Bedini L, Herranz D, Salerno E et al. Separation of correlated astrophysical sources using multiple -lag data covariance matrices. EURASIP Journal on Applied Signal Processing, 2005, (15): 2400-2412.
[49] Caiafa C F, Proto A N. Separation of statistically dependent sources using an l2-distance non-gaussianity measure. Signal Processing, 2006, 86(11): 3404-3420.
[50] Caiafa C F, Salerno E, Proto A N, et al. Blind spectral unmixing by local maximization of non -gaussianity. Signal Processing, 2008, 88(1): 50-68.
[51] Kemp Z. Mapping sub -pixel boundaries from remotely sensed images, Atkinson P M. Innovations in GIS. London: Taylor and Francis, 1997: 166-180.
[52] Atkinson P M. Super-resolution target mapping from soft classified remotely sensed imagery. Proceedings of the 5th International Conference on Geocomputation, 2001.
[53] Thornton M W, Atkinson P M, Holland D A.. A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery. Computers & Geosciences, 2007, 33 (10): 1261- 1272.
[54] Makido Y, Shortridge A. Land Cover Mapping at Sub-Pixel Scales: Unraveling the Mixed Pixel. Proceedings of the 8th International Conference on Geocomputation, University of Michigan, Ann Arbor, Michigan, 2005.
[55] Thornton M W, Atkinson P M, Holland D A. Sub -pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel- swapping. International Journal of Remote Sensing, 2006, 27(3): 473-491.
[56] Tatem A J, Lewis H G, Atkinson P M, et al. Super-resolution target identification from remotely sensed images using a hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(4): 781-796.
[57] Tatem A J, Lewis H G, Atkinson P M, et al. Super-resolution land cover pattern prediction using a hopfield neural network. Remote Sensing of Environment, 2002, 79(1): 1- 14.
[58] Tatem A J, Lewis H G, Atkinson P M, et al. Increasing the spatial resolution of agricultural land cover maps using a hopfield neural network. International Journal of Geographical Information Science, 2003, 17(7): 647-672.
[59] Aplin P, Atkinson P M, Curran P J. Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the UK. Remote Sensing of Environment, 1999, 68(3): 206-216.
[60] Aplin P, Atkinson P M. Sub-pixel land cover mapping for per -field classification. International Journal of Remote Sensing, 2001, 22(14): 2853-2858.
[61] Mertens K C, Verbeke L P C, Ducheyne E I, et al. Using genetic algorithms in sub -pixel mapping. International Journal of Remote Sensing, 2003, 24(21): 4241-4247.
[62] Mertens K C, Verbeke L P C, Westra T, et al. Sub-pixel mapping and sub -pixel sharpening using neural network predicted wavelet coefficients. Remote Sensing of Environment, 2004, 91(2): 225-236.
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