Granular neural networks for land use classification

Soft Computing

Volumn 9, Issue 5, 2005, Pages 332-340

  Vasilakos, Athanassios ^a   Stathakis, Demetris ^a  

^a UNIVERSITY OF THESSALY   (Greece)

Author keywords

Computational intelligence; Fuzzy sets; Geographical information systems; Granular neural networks; Land use; Remote sensing

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; DATA PROCESSING; DATA STRUCTURES; FUZZY SETS; GEOGRAPHIC INFORMATION SYSTEMS; LAND USE; LEARNING ALGORITHMS; OPTIMIZATION; REMOTE SENSING;

GENETIC OPTIMIZATION; GRANULAR NEURAL NETWORKS; INFORMATION GRANULATION; LAND USE CLASSIFICATION;

NEURAL NETWORKS;

EID: 17244366734     PISSN: 14327643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00500-004-0412-5     Document Type: Article

References (20)

1. Atkinson PM, Tatnall ARL (1997 A) Introduction. Neural networks in remote sensing. Int J Remote Sensing 18(4): 699-709
2. Atkinson PM, Cutler MEJ, Lewis H (1997B) Mapping subpixel proportional land cover with AVHRR imagery. Int J Remote Sensing 18(4): 917-935
3. Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE transactions on geosciences and remote sensing 28(4): 540-552
4. Bernad AC, Wilkinson GG, Kanellopoulos I (1997) Training strategies for neural soft classification of remotely-sensed imagery. Int J Remote Sensing 18(8): 1851-1856
5. Borel CC, Gerstl SAW (1994) Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sensing of Environment 47: 403-416
6. Bortolan G (1998) An architecture of fuzzy neural networks for linguistic processing, Fuzzy Sets and Systems 100: 197-215
7. Bischof H, Schneider W, Pinz AJ (1992) Multispectral classification of Landsat-images using neural networks. IEEE Transactions on geoscience and remote sensing 30(3): 482-490
8. Chen KS, Yen SK, Tsay DW (1997) Neural classification of SPOT imagery through integration of intensity and fractal information. Int J Remote Sensing 18(4): 763-783
9. Civco DL (1993) Artificial neural networks for land-cover classification and mapping. Int J geographical information systems 7(2): 173-186
10. Congalton RG (1998) Assessing the accuracy of remotely sensed data: principles and practices, Lewis
11. Decatur SE (1989) Application of neural networks to terrain classification. In: Proceedings of the international Joint Conference on neural networks, held in Washington, DC, Piscataway, NJ: IEEE, (1): 283-288
12. Hecht-Nielsen R (1988) Neurocomputing: Picking the human brain. IEEE Spectrum 25(3): 36-41
13. Kanellopoulos I, Wilkinson GG (1997) Strategies and best practice for Neural Network image classification Springer-Verlag
14. Keiner LE, Xiao-Hai Yan (1998) A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery. Remote sensing of environment 66(2): 153-165
15. Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Magazine 22(2): 4-22
16. Pedrycz W, Vasilakos A (1999) Linguistic models and the linguistic modeling. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, 29(6): 745-757
17. Pedrycz W, Vukovich G (2001), Granular neural networks, Neurocomputing 36: 205-224
18. Short NJ (1991) A real-time expert system and Neural Network for the classification of remotely sensed data. 1991 ACSM-ASPRS American congress of surveying and mapping-American society of photogrammetry and Remote Sensing, annual convention held in march 25-29, 1991 (Greenbelt, MD: NASA Goddard Space Flight Center), (3): 406-418
19. Nauck U (1999) Design and Implementation of a Neuro-Fuzzy Data Analysis Tool in Java,Technische Universität Braunschweig, Institut für Betriebssysteme und Rechnerverbund
20. Zadeh LA (1965) Fuzzy sets. Information and control 6(8): 338-353