On the Use of a Hybrid Approach to Contrast Endmember Induction Algorithms
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.
KeywordsGround Truth Hyperspectral Image Dissimilarity Measure Content Base Image Retrieval Dissimilarity Function
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- 2.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)Google Scholar
- 3.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)Google Scholar
- 8.Kozintsev, B.: Computations with Gaussian Random Fields, PhD Thesis. University of Maryland (1999)Google Scholar
- 9.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)Google Scholar
- 11.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)Google Scholar