基于偏振光谱融合的镜面反射去除方法及其在植被健康监测中的应用

doi:10.3964/j.issn.1000-0593(2023)11-3607-08

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Abstract Vegetation remote sensing monitoring has been widely used in various fields, such as crop disease and insect pest monitoring, forest coverage monitoring, and vegetation growth monitoring. Monitoring changes in plant chlorophyll content is of great significance for understanding plant growth, monitoring vegetation pests and diseases, and even monitoring vegetation feedback on global climate change. However, these monitoring are often disturbed by the specular reflection of leaves, which reduces the inversion accuracy of chlorophyll content. This paper aims to eliminate the specular reflection interference in remote sensing monitoring of plant health, a polarization multispectral imaging system was established, a specular reflection removal index (SRRI) was proposed. A fusion algorithm was proposed to detect plants based on the spectral and polarization characteristics of diffuse and specular reflection of vegetation. SRRI, degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to eliminate the interference of specular reflection and improve the accuracy of plant health status detection. In addition, a fusion algorithm based on SRRI, DoLP and AOP calculates a polarization fusion specular reflection removal index (PFSRRI). Correlation analysis was performed on relative chlorophyll content (SPAD), ratio vegetation index (SR), normalized vegetation index (NDVI), SRRI_SR, SRRI_NDVI, PFSRRI_SR and PFSRRI_NDVI to understand their ability to eliminate specular reflection interference. The results showed that SR and SPAD (R²=0.012 8) and NDVI and SPAD (R²=0.007 5) had the worst correlation, indicating that SR and NDVI had the highest sensitivity to mirror reflection. SRRI_SR and SPAD (R²=0.818), and SRRI_NDVI and SPAD (R²=0.889) had a good correlation. The correlation between PFSRRI_SR and SPAD (R²=0.955) and PFSRRI_NDVI and SPAD (R²=0.948) was the best, which highlighted the potential of PFSRRI in eliminating mirror reflection interference and detecting plant health status. PFSRRI_SR and PFSRRI_NDVI 3d scatter plots show good discrimination ability for different health degrees of plants, with high sensitivity and specificity. The variation trend and classification status of vegetation health state can be intuitively seen through the color and trend of the surfaces. Among them, the sensitivity and specificity of PFSRRI_SR to classify specular leaves from stress level-1 was 100% and 100%, and the sensitivity and specificity of PFSRRI_NDVI to classify specular leaves from stress level-1 was 98% and 100%, indicating the excellent detection performance of PFSRR_SR and PFSRRI_NDVI after removing specular interference. In summary, this method can effectively eliminate specular reflection interference and improve the detection accuracy of vegetation health status.

Key words：Remote sensing; Specular reflection; Image fusion; Vegetation Index; Vegetation health monitoring

Received: 2022-05-16 Accepted: 2022-09-13

ZTFLH:

TP79

Corresponding Authors: WANG Chi E-mail: wangchi@shu.edu.cn

Cite this article:

LI Si-yuan,JIAO Jian-nan,WANG Chi. Specular Reflection Removal Method Based on Polarization Spectrum Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)11-3607-08 OR https://www.gpxygpfx.com/EN/Y2023/V43/I11/3607

[1] Thomas S C. Global Change Biology, 2005, 11(2): 197.
[2] Sandmeier S R. Remote Sensing of Environment, 2000, 73(3): 257.
[3] XU Jin, MENG Ji-hua（徐晋，蒙继华）. Remote Sensing Technology and Application（遥感技术与应用），2016，31(1)：74.
[4] Lichtenthaler H K, Wellburn A R. Biochemical Society Transactions, 1983, 11(5): 591.
[5] Madeira A C, Mendonca A, Ferreira M E, et al. Communications in Soil Science and Plant Analysis, 2000, 31(5-6): 631.
[6] SU Wei, ZHAO Xiao-feng, SUN Zhong-ping, et al(苏伟，赵晓凤，孙中平). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(5): 1535.
[7] Baranoski G V G, Rokne J G. International Journal of Remote Sensing, 2005, 26(3)：503.
[8] Kim S R, Lee W K, Lim C H, et al. Forests, 2018, 9(3): 115.
[9] Zang Z, Wang G X, Lin H, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 171: 253.
[10] Sims D A, Gamon J A. Remote Sensing of Environment, 2002, 81(2-3): 337.
[11] Li Y, Chen Y, Huang J. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4): 2173.
[12] Wang S K, Yu C Y, Sun Y J, et al. Multimedia Tools and Applications, 2018, 77(9): 11363.
[13] LIN Yi, LIU Si-yuan, YAN Lei, et al（林沂，刘思远，晏磊，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2020，40(6)：1956.
[14] SU Wei, XIE Zi-xuan, WANG Wei, et al（苏伟，谢茈萱，王伟，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2020，51(5)：173.
[15] Li S Y, Jiao J N, Wang C. Remote Sensing, 2021, 13(17): 3510.
[16] Lavigne D A, Breton M, Fournier G, et al. Proceedings of SPIE—The International Society for Optical Engineering, Orlando, FL, 13 April, 2009, 733673361A-73361A-11.
[17] Jordan C F. Ecology, 1969, 50(4): 663.
[18] Goldstein D H. SPIE International Symposium on Optical Science and Technology, San, Diego, 30 July, 2000, 4133: 112.
[19] Woolley J T. Plant Physiology，1971, 47(5): 656.
[20] Grant L. Remote Sensing of Environment, 1987, 22(2): 309.
[21] Bousquet L, Lachérade S, Jacquemoud S, et al. Remote Sensing of Environment, 2005, 98(2-3): 201.
[22] Rondeaux G, Vanderbilt V C. International Journal of Remote Sensing, 1993, 14(9): 1815.
[23] Grant L, Daughtry C S T, Vanderbilt V C. Environmental and Experimental Botany, 1987, 27(2): 139.
[24] Grant L, Daughtry C S T, Vanderbilt V C. Physiologia Plantarum, 1993, 88(1): 1.