Abstract
A new method of relative radiometric calibration based on the skylight monitor is presented for airborne hyperspectral remote sensing to eliminate the negative effect on data resulting from the time-varying skylight during a long flight. In terms of large area operation of mapping or spectral imaging, a static radiation model hardly reveals actual cases as external environment keeps changing. Compared with common considerable factors, fluctuation of skylight is the primary one that may observably affect spectral imaging continuously leading to radiation changes upon flight lines. To get rid of the influence of skylight on spectral imagery and monitoring the variation during the flight, we assemble a set of skylight monitor fixed to the aircraft cabin for the sake of recording the semi-spherical skylight keeping synchronization with hyperspectral imaging system and the position and orientation system. The skylight monitor consists of a cosine corrector on the top receiving the skylight, a stable optical fiber and a visible and near-infrared linear array modular spectrometer. The corresponding matched data of skylight with each frame of images is used to correct related digital number after stripe non-uniformity correction, achieving mean relative standard deviation (coefficient of variation or CV) of under 6% for all 256 bands. The final result shows a satisfactory calibrated image of an appropriate evaluation criteria with contrast of the original image.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arabi, B., Salama, M.S., Wernand, M.R., Verhoef, W.: Remote sensing of water constituent concentrations using time series of in situ hyperspectral measurements in the Wadden Sea. Remote Sens. Environ. 216, 154–170 (2018). https://doi.org/10.1016/j.rse.2018.06.040
Asner, G.P., Martin, R.E., Knapp, D.E., Tupayachi, R., Anderson, C.B., Sinca, F., Vaughn, N.R., Llactayo, W.: Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355(6323), 385–388 (2017). https://doi.org/10.1126/science.aaj1987
Cheng, X.Y., Wang, Y.M., Guo, R., Huang, J.Z.: Unsupervised classification-based hyperspectral data processing: lossy compression. Opt. Quant. Electron. (2018). https://doi.org/10.1007/s11082-018-1686-7
ElMasry, G., Mandour, N., Al-Rejaie, S., Belin, E., Rousseau, D.: Recent applications of multispectral imaging in seed phenotyping and quality monitoring an overview. Sensors (2019). https://doi.org/10.3390/s19051090
Garzonio, R., Di Mauro, B., Colombo, R., Cogliati, S.: Surface reflectance and sun-induced fluorescence spectroscopy measurements using a small hyperspectral UAS. Remote Sens. (2017). https://doi.org/10.3390/rs9050472
Gorrono, J., Banks, A.C., Fox, N.P., Underwood, C.: Radiometric inter-sensor cross-calibration uncertainty using a traceable high accuracy reference hyperspectral imager. ISPRS J. Photogramm. 130, 393–417 (2017). https://doi.org/10.1016/j.isprsjprs.2017.07.002
He, W., Zhang, H.Y., Zhang, L.P., Shen, H.F.: Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration. IEEE Trans. Geosci. Remote Sens. 54(1), 176–188 (2016). https://doi.org/10.1109/TGRS.2015.2452812
Hu, Z.Y., Su, X.F., Li, X.Y., Zhang, L.L., Chen, F.S.: A method for the characterization of intra-pixel response of infrared sensor. Opt. Quant. Electron. (2019). https://doi.org/10.1007/s11082-019-1790-3
Huang, S.Q., Wu, W.S., Wang, L.P., Duan, X.Y.: Methods of removal wide-stripe noise in short-wave infrared hyperspectral remote sensing image. Sens. Rev. 39(1), 17–23 (2019). https://doi.org/10.1108/SR-03-2017-0039
Jia, J.X., Wang, Y.M., Cheng, X.Y., Yuan, L.Y., Zhao, D., Ye, Q., Zhuang, X.Q., Shu, R., Wang, J.Y.: Destriping algorithms based on statistics and spatial filtering for visible-to-thermal infrared pushbroom hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 57(6), 4077–4091 (2019). https://doi.org/10.1109/TGRS.2018.2889731
Khan, M.J., Khan, H.S., Yousaf, A., Khurshid, K., Abbas, A.: Modern trends in hyperspectral image analysis: a review. IEEE Access. 6, 14118–14129 (2018). https://doi.org/10.1109/ACCESS.2018.2812999
Kopp, G., Smith, P., Belting, C., Castleman, Z., Drake, G., Espejo, J.: Radiometric flight results from the hyperspectral imager for climate science (HySICS). Geosci. Instrum. Method 6(1), 169–191 (2017). https://doi.org/10.5194/gi-6-169-2017
Lee, Z., Shang, S.L., Lin, G., Chen, J., Doxaran, D.: On the modeling of hyperspectral remote-sensing reflectance of high-sediment-load waters in the visible to shortwave-infrared domain. Appl. Opt. 55(7), 1738–1750 (2016). https://doi.org/10.1364/AO.55.00173810.1002/jum.14341
Li, X.Y., Yang, L., Su, X.F., Hu, Z.Y., Chen, F.S.: A correction method for thermal deformation positioning error of geostationary optical payloads. IEEE Trans. Geosci. Remote 57(10), 7986–7994 (2019a). https://doi.org/10.1109/TGRS.2019.2917716
Li, X.Y., Su, X.F., Hu, Z.Y., Yang, L., Zhang, L.L., Chen, F.S.: Improved distortion correction method and applications for large aperture infrared tracking cameras. Infrared Phys. Technol. 98, 82–88 (2019b). https://doi.org/10.1016/j.infrared.2019.02.009
Liu, X., Zhou, M., Qiu, S., Sun, L., Liu, H.Y., Li, Q.L., Wang, Y.T.: Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology. J. Opt. UK (2017). https://doi.org/10.1088/2040-8986/aa95d7
Matteoli, S., Diani, M., Theiler, J.: An overview of background modeling for detection of targets and anomalies in hyperspectral remotely sensed imagery. IEEE J. STARS 7(6), 2317–2336 (2014). https://doi.org/10.1109/JSTARS.2014.2315772
Olschewski, F., Ebersoldt, A., Friedl-Vallon, F., Gutschwager, B., Hollandt, J., Kleinert, A.: The in-flight blackbody calibration system for the GLORIA interferometer on board an airborne research platform. Atmos. Meas. Tech. 6(11), 3067–3082 (2013). https://doi.org/10.5194/amt-6-3067-2013
Palmer, S.C.J., Kutser, T., Hunter, P.D.: Remote sensing of inland waters: challenges, progress and future directions. Remote Sens. Environ. 157, 1–8 (2015). https://doi.org/10.1016/j.rse.2014.09.021
Parra, F., Meza, P., Torres, S., Pezoa, J.E., Mella, H.: Modeling and compensating non-uniformity in push-broom NIR hyperspectral imaging system. Infrared Phys. Technol. 63, 204–210 (2014). https://doi.org/10.1016/j.infrared.2014.01.004
Rodrigues, F.A., Blasch, G., Defourny, P., Ortiz-Monasterio, J.I., Schulthess, U., Zarco-Tejada, P.J., Taylor, J.A., Gerard, B.: Multi-temporal and spectral analysis of high-resolution hyperspectral airborne imagery for precision agriculture: assessment of wheat grain yield and grain protein content. Remote Sens. (2018). https://doi.org/10.3390/rs10060930
Sarkar, S., Healey, G.: Hyperspectral texture synthesis using histogram and power spectral density matching. IEEE Trans. Geosci. Remote Sens. 48(5), 2261–2270 (2010). https://doi.org/10.1109/TGRS.2009.2037613
Talone, M., Zibordi, G., Ansko, I., Banks, A.C., Kuusk, J.: Stray light effects in above-water remote-sensing reflectance from hyperspectral radiometers. Appl. Opt. 55(15), 3966–3977 (2016). https://doi.org/10.1364/AO.55.003966
Thompson, D.R., Natraj, V., Green, R.O., Helmlinger, M.C., Gao, B.C., Eastwood, M.L.: Optimal estimation for imaging spectrometer atmospheric correction. Remote Sens. Environ. 216, 355–373 (2018). https://doi.org/10.1016/j.rse.2018.07.003
Wang, Q., van der Velde, R., Su, Z.B.: Use of a discrete electromagnetic model for simulating Aquarius L-band active/passive observations and soil moisture retrieval. Remote Sens. Environ. 205, 434–452 (2018). https://doi.org/10.1016/j.rse.2017.10.044
Wei, C.W., Huang, J.F., Wang, X.Z., Blackburn, G.A., Zhang, Y., Wang, S.S., Mansaray, L.R.: Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves. Remote Sens. Environ. 195, 56–66 (2017). https://doi.org/10.1016/j.rse.2017.03.042
Wen, M.X., Wang, Y.M., Yao, Y., Yuan, L.Y., Zhou, S.Y., Wang, J.Y.: Design and performance of curved prism-based mid-wave infrared hyperspectral imager. Infrared Phys. Technol. 95, 5–11 (2018). https://doi.org/10.1016/j.infrared.2018.10.001
Wolanin, A., Rozanov, V.V., Dinter, T., Noel, S., Vountas, M., Burrows, J.P., Bracher, A.: Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results. Remote Sens. Environ. 166, 243–261 (2015). https://doi.org/10.1016/j.rse.2015.05.018
Xu, W.B., Chen, W.L., Li, J.W.: Identification method of camouflaged objects based on long-wave infrared hyperspectral polarization characteristic. Spectrosc. Spect. Anal. 39(1), 235–240 (2019). https://doi.org/10.3964/j.issn.1000-0593(2019)01-0235-06
Ye, Q., Wang, Y.M., Zhou, S.Y., Cheng, X.Y., Jia, J.X.: Color discrimination based on hyperspectral imaging method. Spectrosc. Spect. Anal. 38(10), 3310–3314 (2018). https://doi.org/10.3964/j.issn.1000-0593(2018)10-3310-05
Yu, Y.X., Yu, H.Y., Guo, L.B., Li, L., Chu, Y.W., Tang, Y.: Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system. Anal. Methods 10(26), 3224–3231 (2018). https://doi.org/10.1039/c8ay00701b
Yuan, L.Y., Xie, J.N., He, Z.P., Wang, Y.M., Wang, J.Y.: Optical design and evaluation of airborne prism-grating imaging spectrometer. Opt. Express 27(13), 17686–17700 (2019). https://doi.org/10.1364/OE.27.017686
Zelinski, M.E.: Overhead longwave infrared hyperspectral material identification using radiometric models. J. Appl. Remote Sens. (2018). https://doi.org/10.1117/1.JRS.12.025019
Zeng, D., Zhang, S., Chen, F.S., Wang, Y.M.: Multi-scale CNN based garbage detection of airborne hyperspectral data. IEEE Access. 7, 104514–104527 (2019). https://doi.org/10.1109/ACCESS.2019.2932117
Zhang, D., Yuan, L.Y., Wang, S.W., Yu, H.X., Zhang, C.X., He, D.G., Han, G.C., Wang, J.Y., Wang, Y.M.: Wide swath and high resolution airborne hyperspectral imaging system and flight validation. Sensors (2019). https://doi.org/10.3390/s19071667
Zhi, L., Yu, X.C., Liu, B., Wei, X.P.: A dense convolutional neural network for hyperspectral image classification. Remote Sens. Lett. 10(1), 59–66 (2019). https://doi.org/10.1080/2150704X.2018.1526424
Zhu, S.J., Lei, B., Wu, Y.R.: Retrieval of hyperspectral surface reflectance based on machine learning. Remote Sens. (2018). https://doi.org/10.3390/rs10020323
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, Sy., Zhang, D., Liu, Hl. et al. A new method of relative radiometric calibration for hyperspectral imaging based on skylight monitor. Opt Quant Electron 51, 369 (2019). https://doi.org/10.1007/s11082-019-2092-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11082-019-2092-5