Abstract
Hyperspectral sensors are capable of collecting information in hundreds of contiguous spectral bands to expand the capability of multispectral sensors that use tens of discrete spectral bands. The contiguous bands play a vital role in study of types of vegetation, minerals, forest and soil types. In this paper, an iteratively learning parameter algorithm has been implemented to classify the hyperspectral image in an unsupervised way. The methodology followed in learning the parameters of ICA mixture model (ICAMM) has been discussed and the proportionality constant has been fixed as 0.7 to obtain the linear transformation for estimating the class membership probability of each pixel of a hyperspectral data. The ICAMM algorithm models class distributions as non-Gaussian densities, has been employed for unsupervised classification. Here the data have been transformed into new space in which, the data are as independent as possible by exploiting higher order statistics. This algorithm produces an average overall accuracy of around 65 % and outperforms the conventional K-means clustering and ISODATA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hughes, G.F.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)
Jia, X., Richards, J.A.: Efficient maximum likelihood classification for imaging spectrometer data sets. IEEE Trans. Geosci. Remote Sens. 32(2), 274–281 (1994)
Foody, G.M., Arora, M.K.: Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications. Pattern Recogn. Lett. 17, 1389–1398 (1996)
Kasetkasem, T., Arora, M.K., Varshney, P.K., Areekul, V.: Improving subpixel classification by incorporating prior information in linear mixture models. IEEE Trans. Geosci. Remote Sens. 49(3), 1001–1013 (2011)
Chanussot, J., Benediktsson, J.A., Fauvel, M.: Classification of remote sensing images from urban areas using a fuzzy probabilistic model. IEEE Geosci. Remote Sens. Lett. 3(1), 40–44 (2006)
Platt, J.C.: Probalilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A. et al. (ed.), Advances in Large Margin Classifiers. MIT Press, Cambridge (2000)
Xu, M., Watanachaturaporn, P., Varshney, P.K., Arora. M.K.: Decision tree regression for soft classification of remote sensing data. Remote Sens. Environ. 97(3), 322–336 (2005)
Cheng, Q., Varshney, P.K., Arora, M.K.: Logistic regression for feature selection and soft classification of remote sensing data. IEEE Geosci. Remote Sens. Lett. 3(4), 491–494 (2006)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Shah, C.A., Varshney, P.K.: A higher order statistical approach to spectral unmixing of remote sensing imagery. In: Proceedings of Geoscience Remote Sensing Symposium, IGARSS’, vol. 2, pp. 1065–1068 (2004)
Yu, J., Chen, J., Rashid, M.M.: Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes. Process Syst. Eng. 59(8), 2761–2779 (2013). (Wiley Online Library AIChE Journal)
Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. Official J. Int. Neural Netw. Soc. 13(4–5), 411–430 (2000)
Common, P.: Independent component analysis, a new concept? Signal Process 36, 287–314 (1994)
Beckmann, C.F.: Modelling with independent components. NeuroImage 62, 891–901 (2012)
Lee, T.W., Lewicki, M.S., Sejnowski, T.J.: ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. Pattern Anal. Mach. Learn. 22(10), 1078–1089 (2000)
Shah, C.A., Arora, M.K., Robila, S.A., Varshney, P.K.: ICA mixture model based unsupervised classification of hyperspectral imagery. In: IEEE Computer Society Applications on Image Pattern Recognition Workshop, Proceedings, pp. 29–35 (2002)
Plumbley, M.D.: Algorithms for nonnegative independent component analysis. IEEE Trans. Neural Netw. 14(3), 534–543 (2003). (A publication of the IEEE Neural Networks Council)
Shah, C., Arora, M., Varshney, P.: Unsupervised classification of hyperspectral data: an ICA mixture model based approach. Int. J. Remote Sens. 25(2), 481–487 (2004)
Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended informax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Comput. 11(2), 417–441 (1999)
Shah, C.A., Varshney, P.K.: A higher order statistical approach to spectral unmixing of remote sensing imagery. In: Geoscience and Remote Sensing Symposium, pp. 1065–1068 (2004)
Lee, T.W., Lewicki, M.S.: Unsupervised image classification, segmentation and enhancement using ICA mixture models. IEEE Trans. Image Process. 11(3), 270–279 (2002). (A publication of the IEEE Signal Processing Society)
National Aeronautics and Space Administration: http://modis.gsfc.nasa.gov/about/specifications.php. MODIS Homepage
Story, M., Congalton, R.: Accuracy assessment: a user’s perspective. Photogramm. Eng. Remote Sens. 52(3), 397–399 (1986)
Congalton, R.G.: A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 46, 35–46 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Prabhu, N., Arora, M.K., Balasubramanian, R., Gupta, K. (2014). An ICA Mixture Model Based Approach for Sub-pixel Classification of Hyperspectral Data. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_65
Download citation
DOI: https://doi.org/10.1007/978-81-322-1771-8_65
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1770-1
Online ISBN: 978-81-322-1771-8
eBook Packages: EngineeringEngineering (R0)