光谱学与光谱分析 |
|
|
|
|
|
Decomposition of Interference Hyperspectral Images Using Improved Morphological Component Analysis |
WEN Jia1, 2, ZHAO Jun-suo2, WANG Cai-ling3, XIA Yu-li2 |
1. School of Electronics Engineering, Tianjin Polytechnic University, Tianjin 300387, China2. Science and Technology on Integrated Information System Laboratory,Institute of Software, Chinese Academy of Sciences, Beijing 100190, China3. College of Computer Science, Xi’an Shiyou University, Xi’an 710065,China |
|
|
Abstract As the special imaging principle of the interference hyperspectral image data, there are lots of vertical interference stripes in every frames. The stripes’ positions are fixed, and their pixel values are very high. Horizontal displacements also exist in the background between the frames. This special characteristics will destroy the regular structure of the original interference hyperspectral image data, which will also lead to the direct application of compressive sensing theory and traditional compression algorithms can’t get the ideal effect. As the interference stripes signals and the background signals have different characteristics themselves, the orthogonal bases which can sparse represent them will also be different. According to this thought, in this paper the morphological component analysis (MCA) is adopted to separate the interference stripes signals and background signals. As the huge amount of interference hyperspectral image will lead to slow iterative convergence speed and low computational efficiency of the traditional MCA algorithm, an improved MCA algorithm is also proposed according to the characteristics of the interference hyperspectral image data, the conditions of iterative convergence is improved, the iteration will be terminated when the error of the separated image signals and the original image signals are almost unchanged. And according to the thought that the orthogonal basis can sparse represent the corresponding signals but cannot sparse represent other signals, an adaptive update mode of the threshold is also proposed in order to accelerate the computational speed of the traditional MCA algorithm, in the proposed algorithm, the projected coefficients of image signals at the different orthogonal bases are calculated and compared in order to get the minimum value and the maximum value of threshold, and the average value of them is chosen as an optimal threshold value for the adaptive update mode. The experimental results prove that whether LASIS and LAMIS image data, the traditional MCA algorithm can separate the interference stripes signals and background signals very well, and make the interference hyperspectral image decomposition perfectly, and the improved MCA algorithm not only keep the perfect results of the traditional MCA algorithm, but also can reduce the times of iteration and meet the iterative convergence conditions much faster than the traditional MCA algorithm, which will also provide a very good solution for the new theory of compressive sensing.
|
Received: 2014-10-15
Accepted: 2015-02-04
|
|
Corresponding Authors:
WEN Jia
E-mail: 448680289@qq.com
|
|
[1] TU Xiao-long, HUANG Min, Lü Qun-bo(涂小龙,黄 旻,吕群波). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(5): 1401. [2] Wen Jia, Ma Caiwen, Shui Penglang. Optics Communications, 2011, 284(20): 4903. [3] Wen Jia, Ma Caiwen, Zhao Junsuo. Optics Communications, 2014, 322: 97. [4] WEN Jia, MA Cai-wen, ZHAO Jun-suo(温 佳,马彩文,赵军锁). Journal of Harbin Institute of Technology(哈尔滨工业大学学报), 2014, 46(1): 112. [5] Ma Dongmei, Ma Caiwen, Bai Yulong. Journal of Applied Optics, 2011, 32(3): 446. [6] Ma Dongmei, Ma Caiwen, Bai Yulong. Opto-Electronic Engineering, 2011, 38(3): 125. [7] Hu Wenrui, Xie Yuan, Li Lin. Neurocomputing, 2014,144: 222. [8] Filippo Nencini, Andrea Garzelli, Stefano Baronti. Information Fusion. 2007, 8: 143. |
[1] |
ZHAO Ming-fu1, 2, TANG Ping1, 2, TANG Bin1, 2, 3*, HE Peng3, XU Yang-fei1, 2, DENG Si-xing1, 2, SHI Sheng-hui1, 2. Research on Denoising of UV-Vis Spectral Data for Water Quality Detection with Compressed Sensing Theory Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 844-850. |
[2] |
WANG Cai-ling1, 2, WANG Hong-wei3, HU Bing-liang1, WEN Jia4, XU Jun5, LI Xiang-juan2 . A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2919-2924. |
[3] |
HAO Dong-mei1, ZHOU Ya-nan1, WANG Yu1 , ZHANG Song1, YANG Yi-min1, LIN Ling2, LI Gang2, WANG Xiu-li3 . Recognition of Water-Injected Meat Based on Visible/Near-Infrared Spectrum and Sparse Representation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(01): 93-98. |
[4] |
JIANG Lu-lu1, LUO Mei-fu1, ZHANG Yu1,YU Xin-jie2, KONG Wen-wen3, LIU Fei3* . Identification of Transmission Fluid Based on NIR Spectroscopy by Combining Sparse Representation Method with Manifold Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(01): 64-68. |
[5] |
DU Li-min1, 2, LI Jin1, 2*, JIN Guang1, GAO Hui-bin1, JIN Long-xu1, ZHANG Ke1. Compression of Interference Hyperspectral Image Based on FHALS-NTD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(11): 3155-3160. |
[6] |
ZHAO Rui-zhen1, WANG Fei1, LUO A-li2, ZHANG Yan-xia2 . A Method for Auto-Extraction of Spectral Lines Based on Sparse Representation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(07): 2010-2013. |
|
|
|
|