Abstract
In remote sensing hyperspectral image processing, identifying the constituent spectra (endmembers) of the materials in the image is a key procedure for further analysis. The contrast between Endmember Inductions Algorithms (EIAs) is a delicate issue, because there is a shortage of validation images with accurate ground truth information, and the induced endmembers may not correspond to any know material, because of illumination and atmospheric effects. In this paper we propose a hybrid validation method, composed on a simulation module which generates the validation images from stochastic models and evaluates the EIA through Content Based Image Retrieval (CBIR) on the database of simulated hyperspectral images. We demonstrate the approach with two EIA selected from the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Clark, R.N., Roush, T.L.: Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysics Research 89(B7), 6329–6340 (1984)
Daschiel, H., Datcu, M.: Information mining in remote sensing image archives: system evaluation. IEEE Transactions on Geoscience and Remote Sensing 43(1), 188–199 (2005)
Datcu, M., Seidel, K.: Human centered concepts for exploration and understanding of satellite images. In: IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, pp. 52–59 (2003)
Graña, M., Torrealdea, F.J.: Hierarchically structured systems. European Journal of Operational Research 25, 20–26 (1986)
Grana, M., Villaverde, I., Maldonado, J.O., Hernandez, C.: Two lattice computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomputing 72, 2111–2120 (2009)
Ifarraguerri, A., Chang, C.-I.: Multispectral and hyperspectral image analysis with convex cones. IEEE Transactions on Geoscience and Remote Sensing 37(2), 756–770 (1999)
Keshava, N., Mustard, J.F.: Spectral unmixing. IEEE Signal Processing Magazine 19(1), 44–57 (2002)
Kozintsev, B.: Computations with Gaussian Random Fields, PhD Thesis. University of Maryland (1999)
Ritter, G.X., Gader, P.: Fixed points of lattice transforms and lattice associative memories. In: Advances in imaging and electron physics. Advances in imaging and electron physics, vol. 143, p. 264. Academic press, London (2006)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Winter, M.E., Descour, M.R., Shen, S.S.: N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data, Denver, CO, USA, October 1999, vol. 3753, pp. 266–275. SPIE, San Jose (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Veganzones, M.A., Hernández, C. (2010). On the Use of a Hybrid Approach to Contrast Endmember Induction Algorithms. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-13803-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13802-7
Online ISBN: 978-3-642-13803-4
eBook Packages: Computer ScienceComputer Science (R0)