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- 2017
基于张量分解的超光谱图像降秩与压缩
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Abstract:
超光谱图像在常规的二维图像中加入了光谱维度，具有更大的信息量的同时也带来了较大的光谱冗余性，这给图像压缩带来了新的挑战。提出了一种基于张量分解的超光谱图像降秩与压缩方法，将超光谱图像视为三阶张量数据表示，并使用张量分解技术将原始观测张量分解为核张量与多个投影矩阵的乘积形式。这样，超光谱图像被压缩为了低秩张量，它可以通过张量反投影进行图像重构。实验证明张量分解技术能够将超光谱图像压缩到很低的比率，同时保持较低的重构相对误差
| [1] | Du Peijun, Wang Xiaomei, Tan Kun, et al. Dimensionality Reduction and Feature Extraction from Hyper-spectral Remote Sensing Imagery Based on Manifold Learning[J]. Geomatics and Information Science of Wuhan University, 2011, 36(2):148-152(杜培军, 王小美, 谭琨, 等. 利用流形学习进行高光谱遥感影像的降维与特征提取[J]. 武汉大学学报·信息科学版, 2011, 36(2):148-152) |
| [2] | Huang Xin, Zhang Liangpei, Shao Zhenfeng, et al. Dimension Reduction of Texture Features Based upon Independent Component Analysis[J]. Geomatics and Information Science of Wuhan University, 2006, 31(12):1055-1058(黄昕, 张良培, 邵振峰, 等. 基于独立分量分析的纹理特征维数减少[J]. 武汉大学学报·信息科学版, 2006, 31(12):1055-1058) |
| [3] | 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[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1):65-74 |
| [4] | Kolda T G, Bader B W. Tensor Decompositions and Applications[J]. SIAM Review, 2009, 51(3):455-500 |
| [5] | Jolliffe I T. Principal Component Analysis[M]. New York, USA:Springer, 2002 |
| [6] | Zhang Yujin. Image Engineering (part Ⅱ):Image analysis[M]. Beijing:Tsinghua University Press, 2005(章毓晋, 图像工程(中册):图像分析(第二版)[M]. 北京:清华大学出版社, 2005) |
| [7] | Xiong Wei, Zhang Lefei, Du Bo. A Multilinear Discriminant Subspace Projection with Orthogonalization for Face Recognition[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5):583-587(熊维, 张乐飞, 杜博. 一种基于多维正交判别子空间投影的人脸识别方法[J]. 武汉大学学报·信息科学版, 2015, 40(5):583-587) |
| [8] | Guo X, Huang X, Zhang L, et al. Hyperspectral Image Noise Reduction Based on Rank-1 Tensor Decomposition[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 83:50-63 |
| [9] | Zhang Lefei, Huang Xin, Zhang Liangpei. Classification of High Spatial Resolution Imagery Using Support Tensor Machine[J]. Geomatics and Information Science of Wuhan University, 2012, 37(3):314-317(张乐飞, 黄昕, 张良培. 高分辨率遥感影像的支持张量机分类方法[J]. 武汉大学学报·信息科学版, 2012, 37(3):314-317) |
| [10] | Zhang L, Zhang L, Tao D, et al. Compression of Hyperspectral Remote Sensing Images by Tensor Approach[J], Neurocomputing, 2015, 147:358-363 |
