基于多特征和改进稀疏表示的高光谱图像分类
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摘要
为了实现对高光谱图像的分类,提出了一种基于多特征和改进稀疏表示的方法——MFISR。从高光谱图像中提取光谱特征、Gabor特征和局部二值模式(LBP)特征,求解稀疏系数,同时增加一个2范式约束,利用所得系数得到每个测试像素的最终类别标签。实验结果表明:所提MFISR方法对小样本的检测效果显著,分类性能稳定且较优。
A multiple-feature-based improved sparse representation(MFISR)method is proposed herein for the classification of hyperspectral images.The spectral feature,Gabor feature,and local binary pattern(LBP)feature are extracted from the hyperspectral image;subsequently,the sparse coefficients are solved and a 2-paradigm constraint is added.These obtained coefficients are used to determine the final class label of each test pixel.The experimental results demonstrate that the proposed MSIFR method exhibits excellent results for the detection of small samples,and its classification performance is stable and good.
A multiple-feature-based improved sparse representation(MFISR)method is proposed herein for the classification of hyperspectral images.The spectral feature,Gabor feature,and local binary pattern(LBP)feature are extracted from the hyperspectral image;subsequently,the sparse coefficients are solved and a 2-paradigm constraint is added.These obtained coefficients are used to determine the final class label of each test pixel.The experimental results demonstrate that the proposed MSIFR method exhibits excellent results for the detection of small samples,and its classification performance is stable and good.
引文
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[19]Candes E J,Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[20]Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61.
[21]Li W,Du Q,Zhang F,et al.Collaborative representation based k-nearest neighbor classifier for hyperspectral imagery[C].2014 6th Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing(WHISPERS),24-27June 2014,Lausanne,Switzerland,2014:1-4.
[22]Hyperspectral data set[Z/OL].[2018-08-13].http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.
[2]Dong A G,Li J X,Zhang B,et al.Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J].Acta Optica Sinica,2017,37(8):356-363.董安国,李佳逊,张蓓,等.基于谱聚类和稀疏表示的高光谱图像分类算法[J].光学学报,2017,37(8):356-363.
[3]Charles A S,Olshausen B A,Rozell C J.Learning sparse codes for hyperspectral imagery[J].IEEEJournal of Selected Topics in Signal Processing,2011,5(5):963-978.
[4]Kalluri H R,Prasad S,Bruce L M.Decision-level fusion of spectral reflectance and derivative information for robust hyperspectral land cover classification[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(11):4047-4058.
[5]Benediktsson J A,Palmason J A,Sveinsson J R.Classification of hyperspectral data from urban areas based on extended morphological profiles[J].IEEETransactions on Geoscience and Remote Sensing,2005,43(3):480-491.
[6]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.
[7]Chen C,Li W,Tramel E W,et al.Spectral-spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(4):1047-1059.
[8]Jimenez-Rodriguez L O,Arzuaga-Cruz E,VelezReyes M.Unsupervised linear feature-extraction methods and their effects in the classification of highdimensional data[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(2):469-483.
[9]Zhang X R,Gao Z Y,An J L,et al.Joint multifeature hyperspectral image classification with spatial constraint in semantic manifold[C].2016 IEEEInternational Geoscience and Remote Sensing Symposium(IGARSS),10-15July 2016,Beijing,China,2016:481-484.
[10]Zhao C H,Li W.Collaborative representation-based binary hypothesis model with multi-features learning for target detection in hyperspectral imagery[J].Journal of the Indian Society of Remote Sensing,2018,46(5):847-862.
[11]Ojala T,Pietikainen M,Maenpaa T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[12]Li W,Du Q.Gabor-filtering-based nearest regularized subspace for hyperspectral image classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(4):1012-1022.
[13]Yu C Y,Zhao M,Song M P,et al.Hyperspectral image classification method based on targets constraint and spectral-spatial iteration[J].Acta Optica Sinica,2018,38(6):0628003.于纯妍,赵猛,宋梅萍,等.基于目标约束与谱空迭代的高光谱图像分类方法[J].光学学报,2018,38(6):0628003.
[14]Tarabalka Y,Fauvel M,Chanussot J,et al.SVM-and MRF-based method for accurate classification of hyperspectral images[J].IEEE Geoscience and Remote Sensing Letters,2010,7(4):736-740.
[15]Wright J,Yang A Y,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[16]Wu Y,Blasch E,Chen G,et al.Multiple source data fusion via sparse representation for robust visual tracking[C].14th International Conference on Information Fusion,July 2011,5-8,Chicago,2011:12177541.
[17]Zhang E,Zhang X,Liu H,et al.Fast multifeature joint sparse representation for hyperspectral image classification[J].IEEE Geoscience Remote Sensing Letters,2015,12(7):1397-1401.
[18]Gan L,Xia J S,Du P J,et al.Multiple feature kernel sparse representation classifier for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(9):5343-5356.
[19]Candes E J,Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[20]Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61.
[21]Li W,Du Q,Zhang F,et al.Collaborative representation based k-nearest neighbor classifier for hyperspectral imagery[C].2014 6th Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing(WHISPERS),24-27June 2014,Lausanne,Switzerland,2014:1-4.
[22]Hyperspectral data set[Z/OL].[2018-08-13].http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.