计算机科学 ›› 2014, Vol. 41 ›› Issue (12): 275-279.doi: 10.11896/j.issn.1002-137X.2014.12.059
张帆,杜博,张良培,张乐飞
ZHANG Fan,DU Bo,ZHANG Liang-pei and ZHANG Le-fei
摘要： 如何准确识别图像中的类别信息,是计算机视觉和模式识别领域的重要研究问题。遥感卫星图像数据,尤其是高光谱等遥感图像数据的出现,将空间信息与光谱信息集成于同一数据集中,丰富了图像信息来源。如何准确地识别高光谱图像中的地物类别,已经成为了图像处理和模式识别领域的热点问题。面向高光谱图像数据提出了一种基于波段分组特征和形态学特征的高光谱图像分类方法,结合空间和光谱特征提高分类精度。通过真实的高光谱数据实验证明:利用波段分组可以有效地保持光谱特征,降低数据冗余；在波段分组基础上结合形态学特征进行分类,比传统分类方法的分类精度明显提高。
| [1] 钟燕飞,张良培,李平湘.基于多值免疫网络的多光谱遥感影像分类[J].计算机学报,2007,30(12):2181-2188 [2] 王兆虎,刘芳,焦李成.一种基于视觉特性的遥感图像分割[J].计算机学报,2005,8(10):1686-1691 [3] Goetz A F H,Vane G,Solomon J E,et al.Imaging spectrometry for earth remote sensing[J].Science,1985(228):1147-1153 [4] Hsu Pai-hui.Feature extraction for hyperspectralimage[C]∥Proceeding of the 20th Asian Conference Remote Sensing.1999:408-410 [5] Yang Zhu-sheng,Guo Lei,Luo Xin.Research on segmentedPCA based on band selection algorithm of hyperspectral image[J].Engineering of Surveying and Mapping,2006,5:15-18 [6] Chavez P S,Berlin G L,Sowers L B.Statistical method for selecting Landsat MSS Ratios[J].Journal of Applied Photogramme-tric Engineering,1982,1:23-30 [7] Samiappan S,Prasad S,Bruce L M.Non-Uniform random feature selection and kernel density scoring with svmbased ensemble classification for hyperspectralimage analysis[J].IEEE Journal of PP Selected Topics in Applied Earth Observations and Remote Sensing,2013(99):1-9 [8] Prasad S,Bruce M.Decision fusion with confidence-based-weight assignment for hyperspectral target recognition[J].IEEE Transactions Geoscience and Remote Sensing,2008,6:1448-1456 [9] Soille P,Pesaresi M.Advances in mathematical morphology applied to geoscience and remote sensing[J].IEEE Transactions Geoscience and Remote Sensing,2002,0(9):2042-2055 [10] Haralick R M,Shanmugam K,Dinstein I.Textural featuresforimage classification[J].IEEE Transactions on Systems,Man,and Cybernetics,1973,SMC-3(6):610-621 [11] Fauvel M,Tarabalka Y,Benediktsson J A,et al.Advances inSpectral-Spatial classification of hyperspectral Images[J].Proceedings of the IEEE PP,2012(99):1-24 [12] Kruizinga P,Petkov N,Grigorescu S E.Comparison of tex-turefeatures based on Gabor filters[C]∥Proc.Int.Conf.Image Anal.Process..1999:142-147 [13] Clausi D A,Deng H.Design-based texture feature fusionusingGabor filters and co-occurrence probabilities[J].IEEE Transactions Image Processing,2005,4(7):925-936 [14] Pesaresi M J,Benediktsson A.A new approach for the morphological segmentation of high-resolution satellite images[J].IEEE Transactions Geoscience and Remote Sensing,2001,39(2):309-320 [15] Fauvel M,Benediktsson J A,Chanussot J,et al.Spectral andspatial classification of hyperspectraldata using svms and morphological profiles[J].IEEE Transactions Geoscience and Remote Sensing,2008,46(11):3804-3814 [16] Hughes G.On the mean accuracy of statistical pattern recogni-zers[J].IEEE Transactions on Information Theory,1968,14(1):55-63 [17] Tuia D,Pacifici F,Kanevski M,et al.Classification of very high spatial resolution imagery using mathematical morphology and support vector machines[J].Geoscience and remote sensing,IEEE Transactions,2009,47(11):3866-3879 [18] Burges C J C.A tutorial on support vector machines for patternrecognition[J].Data Mining and Knowledge Discovery, 1998,2:212-167 [19] Chang C C,Lin C J.LIBSVM:A library for support vector machines.http://www.csie.ntu.edu.tw/~cjlin/libsvm |
| No related articles found! |
|
||
首页