| 基于张量分解的超光谱图像降秩与压缩 |
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| 引用本文： | 张乐飞,何发智.基于张量分解的超光谱图像降秩与压缩[J].武汉大学学报(信息科学版),2017,42(2):193-197. |
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| 作者姓名： | 张乐飞 何发智 |
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| 作者单位： | 武汉大学计算机学院, 湖北 武汉, 430079 |
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| 基金项目： | 湖北省自然科学基金No. 2014CFB711地理空间信息工程国家测绘地理信息局重点实验室开放基金No. 201410对地观测技术国家测绘地理信息局重点实验室开放基金No. K201503 |
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| 摘 要： | 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。
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| 关 键 词： | 超光谱 图像压缩 张量分解 低秩 |
| 收稿时间： | 2015-01-28 |
Hyper-spectral Image Rank-Reducing and Compression Based on Tensor Decomposition |
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| Affiliation: | School of Computer, Wuhan University, Wuhan 430079, China |
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| Abstract: | The hyper-spectral image, which has two spatial dimensions and an additional spectral dimension, brings the greater amount of information than the grey level image but also the heavier spectral redundancy at the same time. Thus, it is a fact that hyper-spectral technology brings new challenges in image compression area. In this paper, we propose a hyper-spectral image compression algorithm based on tensor decomposition, in detail, the hyper-spectral image is represented as a 3-order-tensor, then the tensor decomposition technology is introduced to decompose the observed tensor data into a core tensor multiply by a series of projection matrices. By this way, the given hyper-spectral image is compressed into a low rank tensor, and it could be reconstructed by using the core tensor and the projection matrices. Experiments on real world hyper-spectral image datasets suggests that the proposed approach could reduce the hyper-spectral image to a low rate while keep the low reconstruction error. |
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