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电子学报  2015 

基于多字典稀疏表示的遥感图像亚像元映射

DOI: 10.3969/j.issn.0372-2112.2015.06.001, PP. 1041-1049

Keywords: 亚像元映射,像元分解,空间连续性,多字典学习,稀疏表示

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Abstract:

本文提出了一种基于多字典稀疏表示的亚像元映射算法,利用已知的同类型高空间分辨率地物分布图像,构建能够更好反映不同类别地物空间分布模式的多个字典,将待分类亚像元用每一类字典稀疏表示,并依据重构误差最小化原则以及光谱失真程度约束条件来划分亚像元的地物类别.模拟与真实数据上的实验结果表明,本文算法能有效应对地物空间分布模式的多样性,具有更高的亚像元映射精度和更好的算法鲁棒性.

References

[1]  Shaw G, Manolakis D.Signal processing for hyperspectral image exploitation[J].IEEE Transactions on Signal Processing Magazine, 2002, 19(1):12-16.
[2]  Keshava N, Mustard J F.Spectral unmixing[J].IEEE Signal Processing Magazine, 2002, 19(1):44-57.
[3]  Atkinson P M.Mapping Sub-pixel Boundaries from Remote Sensed Images[M].Innovations in GIS 4, London:Taylor & Francis, 1997.166-180.
[4]  凌峰, 吴胜军, 肖飞, 等.遥感影像亚像元定位研究综述[J].中国图象图形学报, 2011, (08):1335-1345. Ling Feng, Wu Sheng-jun, Xiao Fei, et al.Sub-pixel mapping of remotely sensed imagery:a review[J].Journal of Image and Graphics, 2011, (08):1335-1345.(in Chinese)
[5]  Atkinson P M.Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study[J].International Journal of Remote Sensing, 2009, 30(20):5293-5308.
[6]  Mertens K C, de Baets B, Verbeke L P C, et al.A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models[J].International Journal of Remote Sensing, 2006, 27(15):3293-3310.
[7]  Zhong Y, Zhang L.Remote sensing image subpixel mapping based on adaptive differential evolution[J].IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2012, 42(5):1306-1329.
[8]  Villa A, Chanussot J, Benediktsson J A, et al.Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution[J].IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3):521-533.
[9]  Atkinson P M.Sub-pixel target mapping from soft-classified, remotely sensed imagery[J].Photogrammetric Engineering and Remote Sensing, 2005, 71(7):839-846.
[10]  Xu X, Zhong Y, Zhang L.Adaptive subpixel mapping based on a multiagent system for remote-sensing imagery[J].IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2):787-804.
[11]  Boucher A, Kyriakidis P C, Cronkite-Ratcliff C.Geostatistical solutions for super-resolution land cover mapping[J].IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1):272-283.
[12]  Boucher A, Kyriakidis P C.Super-resolution land cover mapping with indicator geostatistics[J].Remote Sensing of Environment, 2006, 104(3):264-282.
[13]  Mertens K C, Verbeke L P C, Westra T, et al.Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients[J].Remote Sensing of Environment, 2004, 91(2):225-236.
[14]  Tatem A J, Lewis H G, Atkinson P M, et al.Super-resolution land cover pattern prediction using a Hopfield neural network[J].Remote Sensing of Environment, 2002, 79(1):1-14.
[15]  Nguyen M Q, Atkinson P M, Lewis H G.Superresolution mapping using a Hopfield neural network with fused images[J].IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(3):736-749.
[16]  Yanfeng G, Ye Z, Junping Z.Integration of spatial-spectral information for resolution enhancement in hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(5):1347-1358.
[17]  Foody G M.Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution[J].International Journal of Remote Sensing, 1998, 19(13):2593-2599.
[18]  Meng Y, Zhang D, Xiangchu F, et al.Fisher discrimination dictionary learning for sparse representation[A].IEEE International Conference on Computer Vision (ICCV)[C].Washington, D C:IEEE Computer Society, 2011.543-550.
[19]  Elad M, Aharon M.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing, 2006, 15(12):3736-3745.
[20]  Mairal J, Sapiro G, Elad M.Learning multiscale sparse representations for image and video restoration[J].Multiscale Modeling & Simulation, 2008, 7(1):214-241.
[21]  Wright J, Yang A Y, Ganesh A, et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210-227.
[22]  Aharon M, Elad M, Bruckstein A.K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[23]  Tropp J A, Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory, 2007, 53(12):4655-4666.
[24]  Congalton R G K G.Assessing the Accuracy of Remote Sensed Data:Principles and Pratices[M].New York:Lewis Publishers, 1999.
[25]  Cohen J.A coefficient of agreement for nominal scales[J].Educational and Psychological Measurement, 1960, 20(1):37-46.
[26]  Yu Y, Sun W.Target spectra guided spectral unmixing for hyperspectral images[J].Chinese High Technology Letters, 2012, 22(3):240-248.

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