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| 基于稀疏表示的高光谱数据压缩算法 | |
| 引用本文： | 吴倩,张荣,徐大卫.基于稀疏表示的高光谱数据压缩算法[J].电子与信息学报,2015,37(1):78-84. |
| 作者姓名： | 吴倩 张荣 徐大卫 |
| 作者单位： | 1. 中国科学技术大学电子工程与信息科学系 合肥230027 2. 中国科学院电磁空间信息重点实验室 合肥230027 |
| 基金项目： | 国家自然科学基金(61172154)，国家973计划项目(2010CB731904)，国家自然科学基金重点项目(61331020)和中国科学院光电研究院雏鹰计划资助课题 |
| 摘 要： | 如何降低高光谱图像大规模数据的存储和传输代价一直是学者们关心的问题。该文提出一种基于稀疏表示的高光谱数据压缩算法，通过一种波段选择算法构造训练样本集合，利用训练得到的基函数字典对高光谱数据所有波段进行稀疏编码，并对表示结果中非零元素的位置和数值进行量化和熵编码，从而实现高光谱图像压缩。实验结果表明该文算法与3维小波相比具有更好的非线性逼近性能，其率失真性能明显优于3D-SPIHT，并且在光谱信息保留上具有巨大的优势。 |
| 关 键 词： | 图像处理 数据压缩 高光谱遥感 稀疏表示 |
| 收稿时间： | 2014-02-19 |
Hyperspectral Data Compression Based on Sparse Representation |
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| Wu Qian , Zhang Rong , Xu Da-wei.Hyperspectral Data Compression Based on Sparse Representation[J].Journal of Electronics & Information Technology,2015,37(1):78-84. | |
| Authors: | Wu Qian Zhang Rong Xu Da-wei |
| Affiliation: | (Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China) (Key Laboratory of Electromagnetic Space Information, Chinese Academy of Science, Hefei 230027, China) |
| Abstract: | How to reduce the storage and transmission cost of mass hyperspectral data is concerned with growing interest. This paper proposes a hyperspectral data compression algorithm using sparse representation. First, a training sample set is constructed with a band selection algorithm, and then all hyperspectral bands are coded sparsely using a basis function dictionary learned from the training set. Finally, the position indices and values of the non-zero elements are entropy coded to finish the compression. Experimental results reveal that the proposal algorithm achieves better nonlinear approximation performance than 3D-DWT and outperforms 3D-SPIHT. Besides, the algorithm has better performance in spectral information preservation. |
| Keywords: | Image processing Data compression Hyperspectral remote sensing Sparse representation |
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