| 结合张量空间与倒易晶胞的高光谱影像去噪去混叠 |
| |
| 引用本文： | 张爱武,赵江华,赵宁宁,康孝岩,郭超凡.结合张量空间与倒易晶胞的高光谱影像去噪去混叠[J].红外与激光工程,2018,47(10):1026002-1026002(10). |
| |
| 作者姓名： | 张爱武 赵江华 赵宁宁 康孝岩 郭超凡 |
| |
| 作者单位： | 1.首都师范大学 三维信息获取与应用教育部重点实验室,北京 100048; |
| |
| 基金项目： | 国家自然科学基金面上项目（41571369）;国家重点研发计划项目（2016YFB0502500）;青海省科技计划项目（2016-NK-138） |
| |
| 摘 要： | 传统去噪去混叠算法大多针对单波段图像，针对于高光谱影像的特点以及噪声、混叠对于图像的影响，提出了一种结合张量与倒易晶胞的多维滤波算法，并将其应用在高光谱影像的去噪和去混叠中。该方法引入张量，将高光谱影像数据视为三阶的张量表达，以倒易晶胞获取影像混叠和噪声较小的频谱覆盖，从最小均方误差的角度交替迭代求解三个方向的滤波器，最终完成影像滤波，在保证影像空间和光谱信息一致性的前提下，有效地减少影像混叠和噪声，提高图像的质量。通过与二维维纳滤波算法、张量多维去噪算法的多组高光谱数据对比实验，证明了文中算法的有效性。
|
| 关 键 词： | 高光谱影像 张量 倒易晶胞 去噪 去混叠 |
| 收稿时间： | 2018-05-07 |
Hyperspectral image denoising and antialiasing based on tensor space and reciprocal cell |
| |
| Affiliation: | 1.Key Laboratory of 3D Information Acquisition and Application of Ministry of Education,Capital Normal University,Beijing 100048,China;2.Engineering Research Center of Spatial Information Technology,Ministry of Education,Beijing 100048,China |
| |
| Abstract: | Conventtrial denoising and antialiasing algorithms are usually for single band images. Previously, numerous studies have only designed for single band images. Aiming at the data characteristics of hyperspectral image and the influence of noise and aliasing on the image, a multidimensional filtering algorithm combining tensor and reciprocating cells was proposed and applied to denoising and antialiasing of hyperspectral images. The method introduced the tensor, and the hyperspectral image data was regarded as the third-order tensor expression. The reciprocal cell was used to obtain the spectrum extrapolation which containd less image aliasing and noise. From the point of view of the minimum mean square error, the algorithm alternately iterated to solve the three directions of the filter, and finally completed the image filtering. The algorithm could effectively reduce the image aliasing and noise under the premise of ensuring the consistency of image space and spectral information. The effectiveness of the proposed algorithm was proved by comparing with multiple sets of hyperspectral data of the two-dimensional Wiener filter algorithm and tensor multidimensional denoising algorithm. |
| |
| Keywords: | |
|
| 点击此处可从《红外与激光工程》浏览原始摘要信息 |
|
点击此处可从《红外与激光工程》下载全文 |
|
