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| A Robust Characteristic Spectrum Construction Algorithm Based on
Spectral Domain Interpolation |
| LI Xu-sheng1, 2, 3, WANG Da-ming1, 2, 3*, WANG Fei-cui1, 2, 3, TONG Yun-xiao1, 2, 3, CAO Si-qi1, 2, 3 |
1. Tianjin Center，China Geological Survey，Tianjin 300170, China
2. North China Center for Geoscience Innovation, China Geological Survey, Tianjin 300170, China
3. Tianjin Key Laboratory of Coast Geological Processes and Environmental Safety，Tianjin 300170, China
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Abstract In the traditional characteristic spectrum extraction algorithm, the arithmetic mean value of spectra is often used to indicate the characteristic spectrum. However, by strengthening the extreme value information and weakening some characteristic information, the indication capability of the mean value is easily affected by the degree of internal differences between objects, Based on the first theorem of geography and the idea of spatial interpolation, a characteristic spectrum extraction algorithm of spectral domain interpolation is proposed. First, the spectral domain of the objects, maximum and minimum reflectances of the objects at each wavelength，are calculated on several object spectra. To obtain single-feature spectral domain spaces, normalized inverse distance interpolation is performed at the center of a single object spectrum with the range of spectral domain. Finally, as multiple spectral domains are added, the cumulative spectral domain space of ground objects is obtained, and the maximum value in the cumulative spectral domain space, which is calculated by wavelength, is taken as the reflectivity, forming the characteristic spectrum of ground objects.To verify the validity and superiority of the spectral domain interpolation characteristic extraction algorithm's performance on the construction of characteristic spectral shape and amplitude, tree species' spectra measured from aerial hyperspectral remote sensing images and ASD are used as data sources to calculate the mean characteristic spectrum (MCS) and spectral domain interpolation characteristic spectrum (ICS). To explore the ICS's ability to characterize the overall shape and reproduce detail features, spectral angle mapping (SAM) of aerial hyperspectral data, feature parameter extraction importance evaluation, and linear discriminant analysis (LDA) of ASD-measured data were performed.The experimental results show that ICS improves the overall accuracy by 4.24% in the SAM when indicating characteristic spectral morphology compared with MCS when it comes to the amplitude feature parameter importance evaluation and LDA, which reveals the characteristic spectral details, the amplitude parameter importance score increased by 0.35 on average, the discrimination accuracy of each tree species increased by 2.51%, and the overall accuracy increased by 2.5%. Studies have shown that ICS is superior to traditional MCS in characterizing the spectral features' overall shape and reproducing detailed features. ICS can be used to refine the feature spectrum extraction process of target objects in classification scenes and improve the separability between classes. Moreover, ICS can also be used to optimize the selection of feature parameters in inversion scenes to improve the ability to characterize spectra.
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Received: 2024-03-22
Accepted: 2024-07-29
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Corresponding Authors:
WANG Da-ming
E-mail: wangdaming@mail.cgs.gov.cn
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[1] Ghamisi P, Plaza J, Chen Y, et al. IEEE Geoscience & Remote Sensing Magazine, 2017, 5(1): 8.
[2] TONG Qing-xi, ZHANG Bing, ZHANG Li-fu(童庆禧, 张 兵, 张立福). Journal of Remote Sensing(遥感学报), 2016, 20(5): 689.
[3] Heo S, Choi J Y, Kim J, et al. Food Science and Biotechnology, 2021, 30(6): 783.
[4] Price D T, McKenney D W, Nalder I A, et al. Agricultural and Forest Meteorology, 2000, 101: 81.
[5] CEN Yi, ZHANG Li-fu, ZHANG Xia, et al(岑 奕, 张立福, 张 霞, 等). Journal of Remote Sensing(遥感学报), 2020, 24(11): 1299.
[6] LI Xu-sheng, LI Hu, CHEN Dong-hua, et al(栗旭升, 李 虎, 陈冬花, 等). Scientia Silvae Sinicae(林业科学), 2020, 56(10): 93.
[7] Wambugu N, Chen Yiping, Xiao Zhenlong, et al. International Journal of Applied Earth Observation and Geoinformation，2021, 105: 102603.
[8] ZHAO Chun-hui, TIAN Ming-hua, LI Jia-wei(赵春晖, 田明华, 李佳伟). Journal of Harbin Engineering University(哈尔滨工程大学学报), 2017, 38(8): 1179.
[9] ZHANG Run-lian, ZHANG Zhao, PENG Xiao-jin, et al(张润莲, 张 昭, 彭小金, 等). Computer Engineering and Design(计算机工程与设计), 2014, 35(12): 4145.
[10] Shu X, Lu H. Applied Intelligence, 2014, 40(4): 724.
[11] Wang Ke, Yong Bin. Remote Sensing, 2016, 8(4): 344.
[12] YU Jia-wei, CHENG Zhi-qing, ZHANG Jin-song, et al(虞佳维, 程志庆, 张劲松, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(12): 3890.
[13] Brosinsky A, Lausch A, Doktor D, et al. Journal of the Indian Society of Remote Sensing, 2014, 42(2): 311.
[14] WANG Dong，WU Jian(王 岽，吴 见). Geography and Geo-Information Science(地理与地理信息科学)，2015，(2): 29. |
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