基于非负矩阵分解的中红外地表特性光谱重建方法

doi:10.3964/j.issn.1000-0593(2024)02-0563-08

Abstract
References
Related Citation (15)

Download: PDF (13185 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the field of mid-infrared remote sensing, hyperspectral surface reflectance/emissivity has high application value and application demand. However, it is difficult to obtain hyperspectral surface reflectance/emission characteristics in absorption band by satellite remote sensing, and there are still many problems in the method of obtaining full band surface characteristics by spectral reconstruction. In order to solve the problem of mid-infrared full band spectral reconstruction of land surface characteristics, based on the Johns Hopkins University (JHU) surface spectrum library and moderate resolution imaging spectrometer (MODIS) short wave infrared and mid-infrared surface multi spectral satellite products, A method for hyperspectral surface reflectance reconstruction using nonnegative matrix factorization (NMF) is proposed. The spectral resolution of the reconstructed hyperspectral reflectance/emissivity in the mid-infrared range of 2.5~5.0 μm can reach 10 nm. Firstly, four typical types of ground objects (soil, vegetation, artificial materials and rocks) were selected based on the JHU spectral library to establish the sample information of surface features. Then, using the spectral response function of the MODIS sensor, the reflectance results of 2.0~5.0 μm band were resampled to 301 bands with 10 nm spectral interval according to the equivalent calculation formula to obtain the JHU surface reflectance spectrum data set. Four endmember vector spectral curves were extracted by non-negative matrix decomposition of the spectral data set. Combined with the global monthly average surface reflectance/emissivity products of MODIS short wave infrared and mid infrared bands (2.13, 3.75, 3.96 and 4.05 μm), the weight coefficient vector corresponding to each pixel can be calculated, and the spectrum reconstruction of any band can be carried out to obtain the global land 5 km×5 km resolution of the monthly mean surface reflectance reconstruction results. At the same time, in order to comprehensively evaluate the spectral reconstruction method, the sub datasets of MODIS short wave infrared and mid infrared bands (2.13, 3.75, 3.96 and 4.05 μm) were extracted from the spectral data set, and the corresponding weight coefficient vector results were calculated, and the full band reflectance spectral reconstruction in the spectral range of 2.0~5.0 μm was performed. The average absolute error and relative error of the reconstruction results are better than 0.01 and 10%, respectively, which can meet the accuracy requirements of spectral reconstruction under the condition that only MODIS satellite data of 4 bands are available. In order to meet the needs of visualization of reconstruction results, based on WebGIS (Web Geographic information system, WebGIS) technology, using cesium framework and browser/server architecture, a two-dimensional and three-dimensional integrated visualization system was built, which integrated satellite base map, terrain data and spectral reconstruction results, to conduct intuitive multi-factor analysis, It provides support for the demonstration and verification of satellite products.

Key words：Hyperspectral; Non-negative matrix factorization; Surface reflectance; Spectral reconstruction; MODIS;WebGIS

Received: 2022-09-07 Accepted: 2023-02-13

ZTFLH:

TP79

Corresponding Authors: LI Zheng-qiang E-mail: lizq@radi.ac.cn

Cite this article:

LI Yin-na,LI Zheng-qiang,ZHENG Yang, et al. Spectral Reconstruction Method of Mid-Infrared Surface Characteristics Based on Non-Negative Matrix Factorization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 563-570.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)02-0563-08 OR https://www.gpxygpfx.com/EN/Y2024/V44/I02/563

[1] Khan M J，Khan H S，Yousaf A，et al. IEEE Access，2018，6：14118.
[2] ZHAO Yong-guang，MA Ling-ling，LI Chuan-rong，et al(赵永光，马灵玲，李传荣，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2015，35(7)：1763.
[3] Hou W，Mao Y，Xu C，et al. Fifth Symposium on Novel Optoelectronic Detection Technology and Application SPIE，2019，11023：702.
[4] Hou W，Wang J，Xu X，et al. Journal of Quantitative Spectroscopy and Radiative Transfer, 2020，253：107161.
[5] GUAN Zheng，DENG Yang-lin，NIE Ren-can(官铮，邓扬琳，聂仁灿). Computer Science(计算机科学)，2021，48(9)：153.
[6] Zhang Y，Xiao Q，Wen J. Journal of Remote Sensing，2017，21(1)：12.
[7] BU Xiang，ZHANG Yong-fu，LIANG Tian-tian，et al(卜祥，张永福，梁田田，等). Chinese Agricultural Science Bulletin(中国农学通报)，2022，38(11)：75.
[8] LI Mei-li，YIN Li-chang，ZHANG Yuan，et al(李美丽，尹礼昌，张园，等). Acta Ecologica Sinica(生态学报)，2021，41(3)：1138.
[9] WANG Zhen，YAN Wen-de，LIU Shu-guang，et al(王震，闫文德，刘曙光，等). Acta Ecologica Sinica(生态学报)，2017，37(10)：3295.
[10] YANG Gang，SUN Wei-wei，ZHANG Dian-fa(杨刚，孙伟伟，张殿发). Geomatics and Information Science of Wuhan University[武汉大学学报（信息科学版)]，2019，44(5): 737.
[11] LIU Jian-jun，WU Ze-bin，WEI Zhi-hui，et al(刘建军, 吴泽彬, 韦志辉, 等). Acta Electronica Sinica(电子学报)，2013，41(3)：432.
[12] FANG Shuai，WANG Jin-ming，CAO Feng-yun（方帅，王金明，曹凤云）. Laser & Optoelectronics Progress(激光与光电子学进展)，2019，56(16)：22.
[13] FU Zhong-liang，LIU Jin-jun，LI Jin-tao，et al(付仲良，刘进军，李金涛，等). Journal of Geomatics(测绘地理信息)，2016，41(2)：58.
[14] LI Xiang-ying，WANG Jian-min，FENG Xing-hao，et al(李相迎，王建民，冯兴浩，等). Computer Systems & Applications(计算机系统应用)，2022，31(8)：108.