Satellite image classification using Genetic Algorithm trained radial basis function neural network, application to the detection of flooded areas

Journal of Visual Communication and Image Representation

Volumn 42, Issue , 2017, Pages 173-182

  Singh, Akansha ^a   Singh, Krishna Kant ^b  

^a THE NORTHCAP UNIVERSITY   (India)

^b DRONACHARYA COLLEGE OF ENGINEERING   (India)

Author keywords

Change detection; Classification; Genetic Algorithm; Landsat 8; Radial basis function

Indexed keywords

CLASSIFICATION (OF INFORMATION); FLOODS; FUNCTIONS; GENETIC ALGORITHMS; LEARNING ALGORITHMS; RADIAL BASIS FUNCTION NETWORKS; SATELLITE IMAGERY; SUPERVISED LEARNING;

CHANGE DETECTION; LANDSAT; LEARNING ABILITIES; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; SATELLITE IMAGE CLASSIFICATION; SEMI-SUPERVISED METHOD; TUNABLE PARAMETER;

IMAGE CLASSIFICATION;

EID: 85003756283     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2016.11.017     Document Type: Article

References (27)

1. [1] Landgrebe, D.A., Signal Theory Methods in Multispectral Remote Sensing. 2003, Wiley, Hoboken, NJ 508.
2. [2] Singh, A., Singh, K.K., Ren, Z., Change detection from remotely sensed images based on a decision theoretic method. Proc. of Computing for Sustainable Global Development (INDIACom), 2015, 2015, 495–498.
3. [3] Ji, Z., Huang, Y., Sun, Q., Cao, G., A spatially constrained generative asymmetric Gaussian mixture model for image segmentation. J. Vis. Commun. Image Represent. 40 (2016), 611–626.
4. [4] Singh, K.K., Nigam, M.J., Pal, K., Mehrotra, A., A fuzzy kohonen local information C-means clustering for remote sensing imagery. IETE Tech. Rev. 31:1 (2014), 75–81.
5. [5] Lu, D., Weng, Q., A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28:5 (2007), 823–870.
6. [6] Singh, K.K., Singh, A., Detection of 2011 Sikkim earthquake-induced landslides using neuro-fuzzy classifier and digital elevation model. Nat. Hazards 83:2 (2016), 1027–1044.
7. [7] Mehrotra, A., Singh, K.K., Nigam, M.J., Pal, K., Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network. Nat. Hazards 77:1 (2015), 367–381.
8. [8] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms. 1981, Plenum Press, New York.
9. [9] Höppner, F., Klawonn, F., Improved fuzzy partitions for fuzzy regression models. Int. J. Approx. Reason. 32:2 (2003), 85–102.
10. [10] Kirindis, S., Chatzis, V., A robust fuzzy local information c means clustering algorithm. IEEE Trans. Image Process. 19:5 (2010), 1328–1337.
11. [11] Chang, G.W., Chen, C.I., Teng, Y.F., Radial-basis-function-based neural network for harmonic detection. IEEE Trans. Ind. Electron. 57:6 (2010), 2171–2179.
12. [12] Billings, S.A., Zheng, G.L., Radial basis function network configuration using genetic algorithms. Neural Networks 8:6 (1995), 877–890.
13. [13] Pradhan, B., Shafie, M., Flood hazard assessment for cloud prone rainy areas in a typical tropical environment. Disaster Adv. 2:2 (2009), 7–15.
14. [14] Watson, J.P., A visual interpretation of a LANDSAT mosaic of the Okavango-delta and surrounding area. Remote Sens. Environ. 35:1 (1991), 1–9.
15. [15] Stancalie, G., Diamandi, A., Corbus, C., Catana, S., Application of EO data in flood fore-casting for the Crisuri Basin, Romania Flood Risk Management: Hazards, Vulnerability and Mitigation Measures, 2004, Springer-Verlag, New York.
16. [16] Wang, Y., Colby, J.D., Mulcahy, K.A., An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data. Int. J. Remote Sens. 23:18 (2008), 3681–3696.
17. [17] Mori, N., Takahashi, T., Yasuda, T., Yanagisawa, H., Survey of 2011 Tohoku earthquake tsunami inundation and run-up. Geophys. Res. Lett., 38(7), 2011.
18. [18] Hoque, R., Nakayama, D., Matsuyama, H., Matsumoto, J., Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Nat. Hazards 57:2 (2011), 525–548.
19. [19] Singh, K.K., Singh, A., Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier. Egypt. J. Remote Sens Space Sci., 2016, 10.1016/j.ejrs.2016.04.003.
20. [20] Singh, K.K., Mehrotra, A., Nigam, M.J., Pal, K., Unsupervised change detection from remote sensing images using hybrid genetic FCM. Proc. of 2013 Students Conference on Engineering and Systems (SCES), 2013, 1–5.
21. [21] Available: < http://floodlist.com/asia/china-july-2016-floods-cost-33-billion-dollars> (accessed 15 September 2016).
22. [22] Hu, Y., Huang, J., Du, Y., Han, P., Huang, W., Monitoring spatial and temporal dynamics of flood regimes and their relation to wetland landscape patterns in Dongting Lake from MODIS time-series imagery. Remote Sens. 7:6 (2015), 7494–7520.
23. [23] USGS Available: < http://earthexplorer.usgs.gov>.
24. [24] Singh, K.K., Nigam, M.J., Pal, K., Detection of 2011 Tohoku tsunami inundated areas in Ishinomaki City using generalized improved fuzzy Kohonen clustering network. Eur. J. Remote Sens. 47 (2014), 461–475.
25. [25] Yan, C., Zhang, Y., Xu, J., Dai, F., Li, L., Dai, Q., Wu, F., A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process. Lett. 21:5 (2014), 573–576.
26. [26] Yan, C., Zhang, Y., Xu, J., Dai, F., Zhang, J., Dai, Q., Wu, F., Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans. Circuits Syst. Video Technol. 24:12 (2014), 2077–2089.
27. [27] Congalton, R.G., Green, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2008, CRC, Boca Raton.