Abstract
In this paper, we propose a new blind decomposition scheme for mixed pixels in multichannel remote sensing images. This scheme does not depend on information from spectrum database or pure endmember pixels and can be used to decompose mixed pixels by using Non-negative Matrix Factorization (NMF). Principal Component Analysis (PCA) is proposed to determine the number of endmembers in remote sensing images and a constraint for NMF that the sum of percentages concerning each endmember equals one is introduced in the proposed scheme. Experimental results obtained from both artificial simulated and practical remote sensing data demonstrate that the proposed scheme for decomposition of mixed pixels has excellent analytical performance.
This research was supported in part by the grants from the Major State Basic Research Development Program of China (No. 2001CB309401), the National Natural Science Foundation of China (No. 30370392 and No. 60171036), Hang Tian Support Techniques Foundation (No. 2004-1.3-03), and Shanghai NSF (No. 04ZR14018).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chang, C.I., Zhao, X.L.: Least Squares Subspace Projection Approach to Mixed Pixel Classification for Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing 36, 898–912 (1998)
Muramatsu, K., Furumi, S., Fujiwara, N., Hayashi, A., Daigo, M., Ochiai, F.: Pattern Decomposition Method in the Albedo Space for Landsat TM and MSS Data Analysis. International Journal of Remote Sensing 21, 99–119 (2000)
Peng, T., Li, B., Su, H.: A Remote Sensing Image Classification Method Based on Evidence Theory and Neural Networks. Proceedings of the 2003 international Conference on Neural Networks and Signal Processing 1, 240–244 (2003)
Heermann, P.D., Khazenie, N.: Classification of Multispectral Remote Sensing Data Using a Back-Propagation Neural Network. IEEE Transactions on Geoscience and Remote Sensing 30, 81–88 (1992)
Chang, C.I., Ren, H., Chang, C.C., D’Amico, F., Jensen, J.O.: Estimation of Subpixel Target Size for Remotely Sensed Imagery. IEEE Transactions on Geoscience and Remote Sensing 42, 1309–1320 (2004)
Du, Q., Chang, C.I.: An Interference Rejection-Based Radial Basis Function Neural Network for Hyperspectral Image Classification. In: International Joint Conference on Neural Networks, pp. 2698–2703 (1999)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
Daniel, D., Lee, H., Seung, S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 401, 788–791 (1999)
Daniel, D., Lee, H., Seung, S.: Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems 13, 556–562 (2001)
Rajapakse, M., Wyse, L.: NMF V.S. ICA for Face Recognition. In: Guo, M. (ed.) ISPA 2003. LNCS, vol. 2745, pp. 605–610. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, H., Wang, B., Zhang, L. (2005). A New Scheme for Blind Decomposition of Mixed Pixels Based on Non-negative Matrix Factorization. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_106
Download citation
DOI: https://doi.org/10.1007/11427445_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)