Abstract
The advancements in hyperspectral remote sensing are increasing continuously and recording a wealth of spatial as well as spectral information about an object, but resulting high volume of data. Analysis and classification of this high volume hyperspectral data needs a ground truth data or spectral library or image based endmembers which assist to unmix the mixed pixels and map their spatial distribution. Till date, though several hyperspectral endmember extraction algorithms have been proposed, every algorithm has its own limitations. The perfect endmember extraction algorithm would find unique spectra with no prior knowledge. This paper discusses the recent improvements and challenges in hyperspectral endmember extraction. The algorithms evaluated includes PPI, NFINDR, FIPPI and ATGP. The experiments are performed on the subset of Hyperion and AVIRIS_NG datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
K.V. Kale et al., A research review on hyperspectral data processing and analysis algorithms. Proc. Natl. Acad. Sci., India, Sect. A 87(4), 541–555 (2017)
A. Plaza, et. al., A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans. Geosci. Remote Sens 42(3), 650–663 (2004)
C.I. Chang, Q. Du, Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens 42(3), 608–619 (2004)
A. Plaza, et. al., An experimental evaluation of endmember generation algorithms, in Proceedings of SPIE, vol. 5995 (2005), pp. 599501-1-9)
P.J. MartÃnez et al., Endmember extraction algorithms from hyperspectral images. Ann. Geophys. 49(1), 93–101 (2006)
C.I. Chang et al., A new growing method for simplex-based endmember extraction algorithm. IEEE Trans. Geosci. Remote Sens. 44(10), 2804–2819 (2006)
J. Plaza, E.M. Hendrix, I. GarcÃa, G. MartÃn, A. Plaza, On endmember identification in hyperspectral images without pure pixels: a comparison of algorithms. J. Math. Imaging Vision 42(2), 163–175 (2012)
Boardman, et. al., Mapping Target Signatures Via Partial Unmixing of AVIRIS Data (1995)
M.E. Williams, N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data, in SPIE Conference on Imaging Spectrometry (1999), pp. 266–275
C.I. Chang, A. Plaza, A fast iterative algorithm for implementation of pixel purity index. IEEE Geosci. Remote Sens. Lett. 3(1), 63–67 (2006)
A. Plaza, C.I. Chang. Impact of initialization on design of endmember extraction algorithms. IEEE Trans. Geosci. Remote Sens 44(11), 3397–3407
R.O. Green, et. al., Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) Data User’s Guide—India Campaign 2015 (2015). Available at: http://vedas.sac.gov.in:8080/aviris/pdf/20150726_AVRISINGDataGuide_v4.pdf, pp. 1–13
J. Pearlman, et. al, Overview of the hyperion imaging spectrometer for the NASA EO-1 mission. in IEEE 2001 International Geoscience and Remote Sensing Symposium, vol. 7 (2001), pp. 3036–3038
A. Plaza, C.I. Chang, Fast implementation of pixel purity index algorithm. in Proceedings of the SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultra spectral Imagery XI, vol. 5806 (Mar 2005), pp. 307–317)
Wu et al., Real-time implementation of the pixel purity index algorithm for endmember identification on GPUs. IEEE Geosci RS Lett 11(5), 955–959 (2014)
Y. Li, C. Gao, S.Y. Chen, C.I. Chang, Endmember variability resolved by pixel purity index in hyperspectral imagery. in Satellite Data Compression, Communications, and Processing X, vol. 9124 (May 2014), p. 91240I (International Society for Optics and Photonics)
A. Plaza, C.I. Chang, An improved N-FINDR algorithm in implementation, in Proceedings of SPIE, vol. 5806 (July 2005), pp. 298–304
Q. Du, N. Raksuntorn, N.H. Younan, R.L. King, Variants of N-FINDR algorithm for endmember extraction. in Proceedings of SPIE, vol. 7109 (Oct 2008), pp. 71090G1-8
L. Ji et al., Modified N-FINDR endmember extraction algorithm for remote-sensing imagery. Int. J. Remote Sens. 36(8), 2148–2162 (2015)
C.I. Chang et al., Comparative study and analysis among ATGP, VCA, and SGA for finding endmembers in hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9(9), 4280–4306 (2016)
D.M. Rogge et al., Integration of spatial–spectral information for the improved extraction of endmembers. Remote Sens. Environ. 110(3), 287–303 (2007)
Acknowledgements
The Authors thankfully acknowledge to DST, GOI, for financial assistance [MRP No. BDID/01/23/2014-HSRS/35 (ALG-V)] and giving AVIRIS-NG data. The authors also extend sincere thanks to UGC SAP and DST-FIST for providing lab facilities to Department of CSIT, Dr. B. A. M. University, Aurangabad-(MS), India. We also express our gratitude towards USGS for providing EO-1 Hyperion data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Solankar, M.M., Gite, H.R., Dhumal, R.K., Surase, R.R., Nalawade, D., Kale, K.V. (2019). Recent Advances and Challenges in Automatic Hyperspectral Endmember Extraction. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_44
Download citation
DOI: https://doi.org/10.1007/978-981-13-1217-5_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1216-8
Online ISBN: 978-981-13-1217-5
eBook Packages: EngineeringEngineering (R0)