Remote-sensing image classification based on an improved probabilistic neural network

Sensors

Volumn 9, Issue 9, 2009, Pages 7516-7539

  Zhang, Yudong ^a   Wu, Lenan ^a   Neggaz, Nabil ^b   Wang, Shuihua ^a   Wei, Geng ^a  

^a SOUTHEAST UNIVERSITY   (China)

^b UNIVERSITÉ DES SCIENCES ET DE LA TECHNOLOGIE D ORAN MOHAMED BOUDIAF   (Algeria)

Author keywords

Brent's Search; Gray level co occurrence matrix; Polarimetric SAR; Principle component analysis; Probabilistic neural network

Indexed keywords

BRENT'S SEARCH; GRAY LEVEL CO-OCCURRENCE MATRIX; POLARIMETRIC SAR; PRINCIPLE COMPONENT ANALYSIS; PROBABILISTIC NEURAL NETWORKS;

ALGORITHMS; IMAGE CLASSIFICATION; NEURAL NETWORKS; POLARIMETERS; SYNTHETIC APERTURE RADAR;

PRINCIPAL COMPONENT ANALYSIS;

EID: 70350022524     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s90907516     Document Type: Article

References (26)

1. Pellizzeri, T.M. Classification of polarimetric SAR images of suburban areas using joint annealed segmentation and "H/A/α" polarimetric decomposition. ISPRS J. Photogramm. Remote Sens. 2003, 58, 55-70.
2. Lee, J.S.; Grunes, M.R.; Kwok, R. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. Int. J. Remote Sens. 1994, 15, 2299-2311.
3. Shimoni, M.; Borghys, D.; Heremans, R.; Perneel, C.; Acheroy, M. Fusion of PolSAR and PolInSAR data for land cover classification. Int. J. Appl. Earth Observ. Geoinf. 2009, 11,169-180.
4. Cloude, S.R.; Pottier, E. A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens. 1996, 34, 498-518.
5. Krogager, E. New decomposition of the radar target scattering matrix. Electron. Lett. 1990, 26,1525-1527.
6. Freeman, A.; Durden, S.L. A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Rem. Sens. 1998, 36, 963-973.
7. Huynen, J.R. Phenomenological Theory of Radar Targets. PhD dissertation. University of Technology: Delft, The Netherlands, 1970.
8. Holm, W.A.; Barnes, R.M. On radar polarization mixed target state decomposition techniques. Proc. of IEEE Radar Conference, Ann Arbor, MI, USA, 1988; pp. 249-254.
9. Cloude, S.R.; Pottier, E. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 1997, 35, 549-557.
10. Lin, C.J.; Chung, I.F.; Chen, C.H. An entropy-based quantum neuro-fuzzy inference system for classification applications. Neurocomputing 2007, 70, 2502-2516.
11. Kido, S.; Tamura, S. Computerized classification of interstitial lung abnormalities on chest radiographs with normalized radiographic index and normalized fractal dimension. Eur. J. Radiol. 2001, 37, 184-189.
12. Ranson, K.J.; Sun, G.Q. An evaluation of AIRSAR and SIR-C/X-SAR images for mapping northern forest attributes in Maine, USA. Remote Sens. Environ. 1997, 59, 203-222.
13. Avci, E.; Turkoglu, I.; Poyraz, M. Intelligent target recognition based on wavelet packet neural network. Expert Syst. Appl. 2005, 29, 175-182.
14. Acqua, F.D.; Gamba, P.; Trianni, G. Semi-automatic choice of scale-dependent features for satellite SAR image classification. Patt. Recogn. Lett. 2006, 27, 244-251.
15. Cooper, G.R.J.; Cowan, D.R. The use of textural analysis to locate features in geophysical data. Comput. Geosci. 2005, 31, 882-890.
16. Khan, K.U.; Yang, J. Polarimetric synthetic aperture radar image classification by a hybrid method. Tsinghua Sci. Technol. 2007, 12, 97-104.
17. Lee, J.J.; Kim, D.; Chang, S.K.; Nocete, C.F.M. An improved application technique of the adaptive probabilistic neural network for predicting concrete strength. Comput. Mater. Sci. 2009, 44, 988-998.
18. Mostafa, M.M. Modeling the competitive market efficiency of Egyptian companies: A probabilistic neural network analysis. Expert Syst. Appl. 2009, 36, 8839-8848.
19. Armand, P.; Benoist, J.; Bousquet, E.; Delage, L.; Olivier, S.; Reynaud, F. Optimization of a one dimensional hypertelescope for a direct imaging in astronomy. Eur. J. Oper. Res. 2009, 195, 519-527.
20. Luukka, P. Classification based on fuzzy robust PCA algorithms and similarity classifier. Expert Syst. Appl.2009, 36, 7463-7468.
21. Pottier, E.; Cloude, S.R. Application of the H/A/α polarimetric decomposition theorems for land classification. Proc. SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry, San Diego, CA, USA, 1997; pp. 132-143.
22. Tien, C.L.; Lyu, Y.R.; Jyu, S.S. Surface flatness of optical thin films evaluated by gray level co- occurrence matrix and entropy. Appl. Surf. Sci. 2008, 254, 4762-4767.
23. Chen, C.H.; Chu, C.T. High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier. Expert Syst. Appl. 2009, 36, 10351-10356.
24. Luukka, P. Classification based on fuzzy robust PCA algorithms and similarity classifier. Expert Syst. Appl. 2009, 36, 7463-7468.
25. Gupta, K.K.; Gupta, R. Despeckle and geographical feature extraction in SAR images by wavelet transform. ISPRS J. Photogramm. Remote Sens. 2007, 62, 473-484.
26. Chen, K.S.; Huang, W.P.; Tsay, D.H.; Amar, F. Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network. IEEE Trans. Geosci. Remote Sens. 1996, 34, 814-820.