An efficient radial basis function neural network for hyperspectral remote sensing image classification

Soft Computing

Volumn 20, Issue 12, 2016, Pages 4753-4759

  Li, Jiaojiao ^a   Du, Qian ^b   Li, Yunsong ^a  

^a XIDIAN UNIVERSITY   (China)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Hyperspectral remote sensing images; Pattern classification; Radial basis function neural network; Support vector machine

Indexed keywords

FUNCTIONS; IMAGE RECONSTRUCTION; LOW PASS FILTERS; MATRIX ALGEBRA; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS; REMOTE SENSING; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CLOSED-FORM EQUATIONS; COMPUTATIONAL COSTS; HYPERSPECTRAL REMOTE SENSING IMAGE; MATRIX INVERSIONS; PARALLEL PROCESSING; RADIAL BASIS FUNCTION NEURAL NETWORKS; SPATIAL AVERAGING FILTER;

IMAGE CLASSIFICATION;

EID: 84930800655     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-015-1739-9     Document Type: Article

References (36)

1. Alexandridis A, Chondrodima E, Efthimiou E, Papadakis G, Vallianatos F, Triantis D (2014) Large earthquake occurrence estimation based on radial basis function neural networks. IEEE Trans Geosci Remote Sens 52(9):5443–5453
2. Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens 28(4):540–552
3. Benediktsson JA, Sveinsson JR (1995) Classification and feature extraction of AVIRIS data. IEEE Trans Geosci Remote Sens 33(9):1194–1205
4. Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, Oxford
5. Broomhead DS, Lowe D (1988) Multivariate functional interpolation and adaptive networks. Complex Syst 2:321–355
6. Bruzzone L, Prieto DF (1999) A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Trans Geosci Remote Sens 37(2):1179–1184
7. Carpenter GA, Gjaja MN, Gopal S, Woodcock CE (1997) ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data. IEEE Trans Geosci Remote Sens 35(2):308–325
8. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge
9. Del Frate F, Pacifici F, Schiavon G, Solimini C (2007) Use of neural networks for automatic classification from high-resolution images. IEEE Trans Geosci Remote Sens 45(4):800–809
10. Du Q (2007) Modified Fisher’s linear discriminant analysis for hyperspectral imagery. IEEE Geosci Remote Sens Lett 4(4):503–507
11. Du Q, Yang H (2008) Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci Remote Sens Lett 5(4):564–568
12. Er MJ, Wu S, Lu J, Toh HL (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13(3):697–710
13. Fernandez-Navarro F, Hervas-Martinez C, Gutierrez PA (2013) Generalised Gaussian radial basis function neural networks. Soft Comput 17:519–533
14. Garg S, Patra K, Pal SK, Chakraborty D (2008) Effect of different basis functions on a radial basis function network in prediction of drill flank wear from motor current signals. Soft Comput 12:777–787
15. Guilfoyle KJ, Althouse ML, Chang C-I (2001) A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks. IEEE Trans Geosci Remote Sens 39(10):2314–2318
16. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
17. Haykin S (1996) Adaptive filter theory, 3rd edn. Prentice Hall, Upper Saddle River
18. Haykin S (2009) Neural networks and learning machines, 3rd edn. Prentice Hall, Upper Saddle River
19. Heermann PD, Khazenie N (1992) Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Trans Geosci Remote Sens 30(1):81–88
20. Karayiannis NB, Randolph-Gips MM (2003) on the construction and training of reformulated radial basis function neural networks. IEEE Trans Neural Netw 14(4):835–846
21. Landgrebe DA (2002) Hyperspectral image data analysis. IEEE Signal Process Mag 19(1):17–28
22. Li J, Du Q, Li W, Li Y (2015) Optimizing extreme learning machine for hyperspectral image classification. J Appl Remote Sens 9:097296
23. Li W, Du Q (2014) Joint within-class collaborative representation for hyperspectral image classification. IEEE J Sel Topics Appl Earth Obs Remote Sens 7(6):2200–2208
24. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790
25. Moody J, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1(2):281–294
26. Muchoney D, Williamson J (2001) A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data. IEEE Trans Geosci Remote Sens 39(9):1969–1977
27. Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257
28. Powell MJD (1987) Radial basis functions for multivariate interpolation: a review. In: Mason JC, Cox MG (eds) Algorithms for approximation. Clarendon, Oxford, pp 143–167
29. Samat A, Du P, Liu S, Li J, Cheng L (2014) E2LMs: ensemble extreme learning machines for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 7(4):1060–1069
30. Tan K, Li E, Du Q, Du P (2014) An efficient semi-supervised classification approach for hyperspectral imagery. ISPRS J Photogramm Remote Sens 97:36–45
31. Tan K, Zhou S, Du Q (2015) Semi-supervised discriminant analysis for hyperspectral imagery with block-sparse graph. IEEE Geosci Remote Sens Lett 12 (in press)
32. Tarabalka Y, Benediktsson JA, Chanussot J (2009) Spectral-spatial classification of hyperspectral imagery based on partitional clustering technology. IEEE Trans Geosci Remote Sens 47(8):2973–2987
33. Wan LJ, Tang K, Li MZ, Zhong YF, Qin AKJ (2015) Collaborative active and semisupervised learning for hyperspectral remote sensing image classification. IEEE Trans Geosci Remote Sens 53(5):2384–2396
34. Yang H, Du Q, Chen G (2012) Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification. IEEE J Sel Top Appl Earth Obs Remote Sens 5(2):544–554
35. Yang ZR (2006) A novel radial basis function neural network for discriminant analysis. IEEE Trans Neural Netw 17(3):604–612
36. Zhang B, O’Neill K, Kong J, Grzegorczyk TM (2008) Support vector machine and neural network classification of metallic objects using coefficients of the spheroidal MQS response modes. IEEE Trans Geosci Remote Sens 46(1):159–171