Open Access Open Access  Restricted Access Subscription Access

Binary Particle Swarm Optimization (BPSO) based Classification of Hyperspectral Images

G. R. Gupta, S. D. Jadhav, N. M. Pagare

Abstract


Remote sensing image is high resolution image, having various bands. Each band provide huge amount of spectral information to identify and differentiate spectrally unique materials. There are large numbers of measured wavelength bands. The major task is to select most informative bands among the available bands. Dimensionality reduction reduces the data volume and redundancy in remote sensing images. The proposed system focuses on feature selection for dimensionality reduction using PSO.


Full Text:

PDF

References


REFERENCES

Hongjun Su, Member, IEEE, Qian Du, Senior Member, IEEE, Genshe Chen, and Peijun Du, Senior Member, IEEE, "Optimized Hyperspectral Band Selection Using Particle Swarm Optimization", IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 6, JUNE 2014.

Jun Li, Prashanth Reddy Marpu, Antonio Plaza, Jose M.Bioucas-Dias, and Jon Atli Benediktsson, "Generalized Composite Kernel Framework for Hyperspectral Image Classification", IEEE transactions. on geoscience and remote sensing, vol. 51, no. 9, September 2013.

H. Yang, Q. Du, and G. Chen, "Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification", IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, Apr. 2012.

H. Yang, Q. Du, H. Su, and Y. Sheng, "An efficient method for supervised hyperspectral band selection", IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, Jan. 2011.

Lu .D and Weng.Q, (2007), "A Survey of Image Classification methods and techniques for improving classification Performance", International Journal of Remote Sensing, 28, 5.

N. Keshava, "Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries", IEEE Trans. Geosci. Remote Sens., vol. 42, no. 7, Jul. 2004.

I. C. Trelea, "The particle swarm optimization algorithm: Convergence analysis and parameter selection", Inf. Process. Lett., vol. 85, no. 6, 2003.

S. V. Stehman, "Estimating the kappa coefficient and its variance under stratified random sampling", Photogramm. Eng. Remote Sens., vol. 62, no. 4, 1996.

R. C. Eberhart and J. Kennedy, "A new optimizer using particle swam theory", in Proc. 6th Int. Symp. Micromach. Human Sci., 1995.

Q. Du and H. Yang, "Similarity-based unsupervised band selection for hyperspectral image analysis", IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, Oct. 2008.

H. Su, H. Yang, Q. Du, and Y. Sheng, "Semi-supervised band clustering for dimensionality reduction of hyperspectral imagery", IEEE Geosci. Remote Sens. Lett., vol. 8, no. 6, Nov. 2011.

S. Jia, Z. Ji, Y. Qian, and L. Shen, "Unsupervised band selection for hyperspectral imagery classification without manual band removal", IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, Apr. 2012.

S. T. Monteiro and Y. Kosugi, "A particle swarm optimization-based approach for hyperspectral band selection", in Proc. IEEE Congr. Evol. Comput., 2007.

Yao.F and Qian,Y.( 2009), "Band Selection based Gaussian processes for Hyperspectral Remote Sensing Images Classification", IEEE, ICIP.

Melgani.F and L. Bruzzone,(2004), "Classification of hyperspectral remote sensing images with support vector machines", IEEE transaction on geo science and remote sensing, 42.

Mercier.,G.and Lennon,M. "SVM for hyperspectral image classification with spectral- based kernels", IEEE, 6.

Schott.J.R, Lee.K, Raqueno. R.V, Hoffmann G.D, Healey.G, (2003), A Subpixel target detection technique based on the invariant approach, AVIRIS, AVIRIS workshop, Pasadena.

Gualteri, J. A., Chettri, S. R., Cromp, R. F., & Johnson, L. F. (1999). "Support vector machines applied to AVIRIS data". Summaries of the Airborne Earth Science Workshop.

Bruzzone, L., Chi, M., & Maeconcini, M. (2006). "A novel transductive SVM for the semisupervised classification of remote sensing images". IEEE Transactions on Geoscience and Remote Sensing.


Refbacks

  • There are currently no refbacks.