Abstract
Spectral Mixture Analysis (SMA) for given hyperspectral mixed pixel vector estimates the number of endmembers in the image along with their spectral signatures and abundance fractions. A novel algorithm called Energy-based Convex Set (ECS) is presented for unsupervised endmember extraction from hyperspectral data in this paper. The algorithm uses the concept of band-energy and convex geometry for extraction of endmembers. The advantage of the proposed algorithm is that it combines the spatial information from band energy and the spectral information from convexity for improvement. The performance of proposed algorithms and prevailing algorithms are evaluated with spectral angle error, spectral information divergence, and normalized cross-correlation for the synthetic and real dataset. It is observed from the simulation results that the proposed algorithm is giving worthy performance than other prevailing algorithms.
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References
Ambikapathi, A., Chan, T., Chi, C., Keizer, K.: Hyperspectral datageometry-based estimation of number of endmembers using p-norm-based pure pixel identification algorithm. IEEE Trans. Geosci. Remote Sens. 51(5), 2753–2769 (2013). https://doi.org/10.1109/TGRS.2012.2213261
Bioucas-Dias, J.M., Nascimento, J.M.: Hyperspectral subspace identification. IEEE Trans. Geosci. Remote Sens. 46(8), 2435–2445 (2008)
Chan, T., Ma, W., Ambikapathi, A., Chi, C.: A simplex volume maximization framework for hyperspectral endmember extraction. IEEE Trans. Geosci. Remote Sens. 49(11), 4177–4193 (2011). https://doi.org/10.1109/TGRS.2011.2141672
Chang, C.I., Wu, C.C., Liu, W., Ouyang, Y.: A new growing method for simplex-based endmember extraction algorithm. IEEE Trans. Geosci. Remote Sens. 44(10), 2804–2819 (2006). https://doi.org/10.1109/TGRS.2006.881803
Chang, C.I.: Real-Time Progressive Hyperspectral Image Processing. Springer, New York (2016). https://doi.org/10.1007/978-1-4419-6187-7
Chang, C.I., Du, Q.: Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 42(3), 608–619 (2004)
Chang, C.I., Plaza, A.: A fast iterative algorithm for implementation of pixel purity index. IEEE Geosci. Remote Sens. Lett. 3(1), 63–67 (2006). https://doi.org/10.1109/LGRS.2005.856701
Clark, R.N., et al.: USGS digital spectral library splib06a. US geological survey, digital data series 231, 2007 (2007)
Computational Intelligence Group, University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Spain: Hyperspectral imagery synthesis (EIAS) toolbox (2010)
Ghosh, G., Kumar, S., Saha, S.K.: Hyperspectral satellite data in mapping salt-affected soils using linear spectral unmixing analysis. J. Indian Soc. Remote Sens. 40(1), 129–136 (2012). https://doi.org/10.1007/s12524-011-0143-x
Martin, G., Plaza, A.: Region-based spatial preprocessing for endmember extraction and spectral unmixing. IEEE Geosci. Remote Sens. Lett. 8(4), 745–749 (2011)
Martin, G., Plaza, A.: Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 380–395 (2012)
Mishra, R., Shah, D., Zaveri, T., Ramakrishnan, R., Shah, P.: Separation of sewage water based on water quality parameters for South Karnataka coastal region, vol. 2017. Asian Association on Remote Sensing, October 2017
Nascimento, J.M., Dias, J.M.: Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43(4), 898–910 (2005)
Oskouei, M.M., Babakan, S.: Detection of alteration minerals using Hyperion data analysis in Lahroud. J. Indian Soc. Remote Sens. 44(5), 713–721 (2016). https://doi.org/10.1007/s12524-016-0549-6
Plaza, A., Martinez, P., Perez, R., Plaza, J.: Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Trans. Geosci. Remote Sens. 40(9), 2025–2041 (2002)
Wang, M., Niu, X., Yang, Q., Chen, S., Yang, G., Wang, F.: Inversion of vegetation components based on the spectral mixture analysis using hyperion data. J. Indian Soc. Remote Sens. 46(1), 1–8 (2017). https://doi.org/10.1007/s12524-017-0661-2
Winter, M.E.: N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: Imaging Spectrometry V, vol. 3753, pp. 266–276. International Society for Optics and Photonics (1999)
Xiong, W., Chang, C., Wu, C., Kalpakis, K., Chen, H.M.: Fast algorithms to implement N-FINDR for hyperspectral endmember extraction. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 545–564 (2011). https://doi.org/10.1109/JSTARS.2011.2119466
Zhu, F.: Hyperspectral unmixing: ground truth labeling, datasets, benchmark performances and survey. arXiv preprint arXiv:1708.05125 (2017)
Acknowledgement
This work has been carried out under the grant received from Vishveshvarya Ph.D. scheme by Government of India. The authors of this paper are also thankful to the management of Nirma University for providing the necessary infrastructure and support.
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Shah, D., Zaveri, T. (2020). Energy Based Convex Set Hyperspectral Endmember Extraction Algorithm. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_5
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