Abstract
The hyperspectral image consists of a high number of bands with low bandwidth which gives the advantage in the identification and detection of the features in the level of mineral and chemical composition. But the availability of hyperspectral data is very less and is highly expensive when compared to multispectral data. Simulation of hyperspectral data with the existing hyperspectral and multispectral data can be used as an alternative if data availability is less and is cost-effective. A new method is proposed for hyperspectral image simulation with Chebyshev and Spectral Angle Mapper (SAM) distance functions using python programming and its libraries. The process is selecting similar spectra of each pixel. Using normal processing, the data simulation is very time-consuming. By increasing the cores employed with parallel processing in python programming, the hyperspectral data simulation time is decreased exponentially from 19 to 1 h 21 min. The study clearly explains the logic and open source python code for the simulation of hyperspectral data. Data can be simulated with python code by just giving the paths of test Sentinel-2 and reference Sentinel-2, AVIRIS data. The simulated image gave better normalized cross-correlation values when compared with the original Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ayesha S, Hanif MK, Talib R (2020) Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf Fusion 59:44–58. https://doi.org/10.1016/j.inffus.2020.01.005
Bhattacharya BK, Singh CP (2017) Spectrum of India, ISRO, Ahmedabad, ISBN 9789382760290 Source: https://vedas.sac.gov.in/vedas/downloads/AVIRIS-NG_CTB_SPECTRUM_OF_INDIA.pdf. Accessed 26 Dec 2020
Crósta AP, Sabine C, Taranik JV (1998) Hydrothermal alteration mapping at Bodie, California, using AVIRIS hyperspectral data. Remote Sens Environ 65:309–319. https://doi.org/10.1016/S0034-4257(98)00040-6
Deborah H, Richard N, Hardeberg JY (2015) A comprehensive evaluation of spectral distance functions and metrics for hyperspectral image processing. IEEE J Sel Top Appl Earth Obs Remote Sens 8:3224–3234. https://doi.org/10.1109/JSTARS.2015.2403257
Gong P, Pu R, Biging GS, Larrieu MR (2003) Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Trans Geosci Remote Sens 41:1355–1362. https://doi.org/10.1109/TGRS.2003.812910
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. https://developers.google.com/earth-engine/datasets/catalog/EO1_HYPERION. Accessed on 05–08–2021
Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352. https://doi.org/10.1016/j.rse.2003.12.013
Harrington L, Blanchard D, Salacain J, Smith S, Amanik P (2015) General Image Quality Equation ( GIQE )National Geospatial-Intelligence Agency (NGA)
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, H Goetz AF (1993) The Spectral Image Processing System (SIPS) Interactive Visualization and Analysis of Imaging Spectrometer Data
Li N, Huang X, Zhao H, Qiu X, Deng K, Jia G, Li Z, Fairbairn D, Gong X (2019) A combined quantitative evaluation model for the capability of hyperspectral imagery for mineral mapping. Sensors (Switzerland) 19. https://doi.org/10.3390/s19020328
Mahantesh K, Manjunath Aradhya VN, Naveena C (2014) An Impact of PCA-Mixture Models and Different Similarity Distance Measure Techniques to Identify Latent Image Features for Object Categorization. In: Thampi S, Gelbukh A, Mukhopadhyay J (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-04960-1_33
Mariotto I, Thenkabail PS, Huete A, Slonecker ET, Platonov A (2013) Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens Environ 139:291–305. https://doi.org/10.1016/j.rse.2013.08.002
Prabhakaran S (2018) https://www.machinelearningplus.com/python/parallel-processing-python/. Accessed on 25–06–2020
Sun J, Yang J, Shi S, Chen B, Du L, Gong W, Song S (2017) Estimating rice leaf nitrogen concentration: Influence of regression algorithms based on passive and active leaf reflectance. Remote Sens 9. https://doi.org/10.3390/rs9090951
Sweet J, Granahah J, Sharp MH (2000) An Objective Standard for Hyperspectral Image Quality. 9th AVIRIS Earth Sci Work. https://popo.jpl.nasa.gov/pub/docs/workshops/00_docs/Sweet_web.pdf. Accessed 01 June 2021
Acknowledgements
Authors would like to thank Editor-in-Chief and anonymous reviewers for their valuable comments in order to improve the manuscript technically understand also thank to Space Application Center (SAC), Indian Space Research Organization (ISRO) Ahmadabad for providing AVIRIS-NG data and financial support to JRF. First author would like to thank Dr. W.R.Reddy, IAS, former Director General; Dr. G. Narendra Kumar, IAS, Director General and Smt. Radhika Rastogi, IAS, Dy. Director General and Head CGARD for accepting host institution and provided computation facilities to execute the project.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by H. Babaie.
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
Peddinti, V.S.S., Mandla, V.R., Mesapam, S. et al. Python parallel processing for hyperspectral image simulation: based on distance functions. Earth Sci Inform 14, 2221–2229 (2021). https://doi.org/10.1007/s12145-021-00690-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-021-00690-7