An Entropy Density Segment Compressed Sensing Method for Reflectance Spectrum Reconstruction
ZHAO Shou-bo1, 2
1. School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
2. School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
Abstract：Reflectance spectrum, as a significant characteristic of the object surface, is widely used in various fields such as remote sensing target identification, content detection of material components, agricultural crop maturity detection, and disease diagnosis in medical imaging. However, while the reflectance spectrum enriches target information, it also brings data redundancy, causing great difficulties in acquiring, processing, and transmitting spectral data. To settle these difficulties, our team has focused on spectral data analysis and processing utilizing compressed sensing technology. It was found that sparse representation of global spectral data was achieved, and spectral reconstruction accuracy was improved. Various sparsities of data in each spectral band constrain different sampling rates in spectral compressed sensing reconstruction methods. This paper proposes an entropy density segment compressed sensing method for reflectance spectrum reconstruction. Specifically, entropy average density is defined as the segmenting reference in the search for breakpoints. Based on the reference, the decision on whether the entropy density of each segmented spectrum is high or low can be given. After that, the sampling rates of each segmented spectrum are reassigned according to the limited equidistant constraint condition. The measurement and sparse matrices are generated for sparsity sensing of segmented reflectance spectrum. The optimal solution is obtained using the orthogonal matching pursuit algorithm. Iteration times of each segmented spectrum are reassigned. Each segmented reflectance spectrum is iteratively matched and reconstructed using the columns in the sensing matrix and sparse signals. Finally, the reconstructed segmented reflectance spectrums are stitched. A comparative experiment was conducted on the reflectance spectrum of the standard color block (24 Munsell ColorChecker) using the global spectral compressed sensing method and our proposed method. The experimental results show that compared with the global spectral compressed sensing method, the proposed method has higher reconstruction accuracy in high entropy density segments and higher compressed efficiency in low entropy density segments. RMSE and MAPE are improved under the same total compressed sampling rate, which enhances the overall curve reconstruction effect.
Key words：Reflective spectrum function; Compressed sensing; Information entropy; Spectral segmentation
[1] Zhou P, Sudduth K A, Veum K S, et al. Computers and Electronics in Agriculture, 2022, 196: 106845.
[2] Tomanič T, Rogelj L, Milanič M. Biomedical Optics Express, 2022, 13(2): 921.
[3] Tolmachev V A, Zharova Y A, Grudinkin S A. Optics and Spectroscopy, 2020, 128(12): 2002.
[4] Ghahremani M, Liu Y, Yuen P, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152: 34.
[5] Ye J C. BMC Biomedical Engineering, 2019, 1(1): 8.
[6] Zhao S, Yang Y. Scientific Reports, 2022, 12(1): 21054.
[7] ZHAO Shou-bo, LI Xiu-hong(赵首博，李秀红). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(4): 1092.
[8] WANG Yang, YANG Meng-yu, ZHAO Shou-bo(王 洋, 杨孟宇, 赵首博). Journal of Electronics & Information Technology(电子与信息学报), 2023, 45(7): 2605.
[9] Tsipouras M G. EURASIP Journal on Advances in Signal Processing, 2019, 2019(1): 10.
[10] Volyar A, Bretsko M, Akimova Y, et al. Optics Letters, 2019, 44(23): 5687.
[11] Omar Y M, Plapper P. Entropy, 2020, 22(12): 1417.
[12] Li Y, Zheng F, Xiong Q, et al. Measurement, 2021, 176: 109199.
[13] Zarei A, Asl B M. Computers in Biology and Medicine, 2021, 131: 104250.
[14] Qin Y, Zou J, Tang B, et al. IEEE Transactions on Industrial Informatics, 2020, 16(1): 215.
