基于独立空谱残差融合的联合稀疏表示高光谱图像分类
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摘要
针对高光谱图像分类中存在的空间信息与光谱信息融合问题,提出一种基于独立空谱残差融合的联合稀疏表示高光谱图像分类算法。使用类独立的光谱角初分类图像,获得像元初始标记后按特定条件进行筛选再构造像元邻域空间。提取图像的全局空间信息,并将其引入到空谱联合稀疏表示模型中,使用单独的光谱信息字典与空间信息字典分别进行图像光谱与空间的联合稀疏表示残差计算。在此基础上,使用残差融合算法确定图像类别。实验结果表明,相对SVM、KNN等算法,该算法能够提升高光谱图像的分类精度,且分类结果更稳定。
Aiming at the problem of spatial information and spectral information fusion in hyperspectral image classification,a joint sparse representation hyperspectral image classification algorithm based on independent space-spectrum residual fusion is proposed. Class-independent spectral angles are used to classify the images,and the initial labels of pixels are obtained,then the neighborhood space of pixels is constructed according to the specific conditions. The global spatial information of the whole image is extracted and introduced into the joint sparse representation model of space-spectrum. The joint sparse representation residual of image spectrum and space is calculated by using a separate spectral information dictionary and a spatial information dictionary respectively. On this basis,the residual fusion algorithm is used to determine image classes. Experimental results show that compared with SVM,KNN and other algorithms,this algorithm can improve the classification accuracy of spectral images and the classification results are more stable.
Aiming at the problem of spatial information and spectral information fusion in hyperspectral image classification,a joint sparse representation hyperspectral image classification algorithm based on independent space-spectrum residual fusion is proposed. Class-independent spectral angles are used to classify the images,and the initial labels of pixels are obtained,then the neighborhood space of pixels is constructed according to the specific conditions. The global spatial information of the whole image is extracted and introduced into the joint sparse representation model of space-spectrum. The joint sparse representation residual of image spectrum and space is calculated by using a separate spectral information dictionary and a spatial information dictionary respectively. On this basis,the residual fusion algorithm is used to determine image classes. Experimental results show that compared with SVM,KNN and other algorithms,this algorithm can improve the classification accuracy of spectral images and the classification results are more stable.
引文
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[2]MELGANI F,BRUZZONE L.Classification of hyperspectral remote sensing images with support vector machines[J].IEEE Transactions on Geosicence and Remote Sensing,2003,42(8):1778-1790.
[3]CAMPS-VALLS G,SHERVASHIDZE N,BORGWARDT K M.Spatio-spectral remote sensing image classification w ith graph kernels[J].IEEE Geoscience and Remote Sensing Letters,2010,7(4):741-745.
[4]刘建军,吴泽彬,韦志辉,等.基于空间相关性约束稀疏表示的高光谱图像分类[J].电子与信息学报,2012,34(11):2666-2671.
[5]TAN K,HU J,LI J,et al.A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,105:19-29.
[6]黄鸿,郑新磊.加权空-谱与最近邻分类器相结合的高光谱图像分类[J].光学精密工程,2016,24(4):873-881.
[7]LI J,HUANG X,GAMBA P,et al.Multiple feature learning for hyperspectral image classification[J].IEEETransactions on Geoscience and Remote Sensing,2015,53(3):1592-1606.
[8]王哲伟.基于稀疏表示残差融合的人脸表情识别[D].江门:五邑大学,2011.
[9]CHEN Y,NASRABADI N M,TRAN T D.Classification for hyperspectral imagery based on sparse representation[C]//Proceedings of Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing.Washington D.C.,USA:IEEE Press,2010:1-4.
[10]CHEN Y,NASRABADI N M,TRAN T D.Hyperspectral image classification using dictionary-based sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(10):3973-3985.
[11]刘万军,杨秀红,曲海成,等.基于光谱信息散度与光谱角匹配的高光谱解混算法[J].计算机应用,2015,35(3):844-848.
[12]王增茂,杜博,张良培,等.基于纹理特征和形态学特征融合的高光谱影像分类法[J].光子学报,2014,43(8):1-2.
[13]尹明,谭熊,张鹏强,等.一种基于纹理和光谱特征的高光谱影像信息向量机分类方法[J].测绘科学技术学报,2015,32(4):368-372.
[14]CHEN C,CHEN N,PENG J.Nearest regularized joint sparse representation for hyperspectral image classification[J].IEEE Geoscience and Remote Sensing Letters,2016,13(3):424-428.
[15]ROSCHER R,WASKE B.Shapelet-based sparse representation for landcover classification of hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(3):1623-1634.
[16]CUI M S,PRASAD S.Class-dependent sparse representation sentation classfication sentation classifier for robust hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5):2085-2086.
[17]FU W,LI S,FANG L.Spectral-spatial hyperspectral image classification via superpixel merging and sparse representation[C]//Proceedings of Geoscience and Remote Sensing Symposium.Washington D.C.,USA:IEEE Press,2015:4971-4974.
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