Browse > Article

Land Cover Classification of Image Data Using Artificial Neural Networks  

Kang, Moon-Seong (Department of Biosystems Engineering, Auburn University)
Park, Seung-Woo (Department of Rural System Engineering, Seoul National University)
Kwang, Sik-Yoon (Department of Biosystems and Agricultural Engineering, Chonnam National University)
Publication Information
Journal of Korean Society of Rural Planning / v.12, no.1, 2006 , pp. 75-83 More about this Journal
Keywords
Artificial neural networks; Maximum likelihood classifiers; Land cover; Remote sensing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Drummond, S. T., K. A. Sudduth, A. Joshi, S. J. Birrell, and N. R. Kitchen, 2003, Statistical and Neural Methods for Site-Specific Yield Prediction. Trans. ASAE 46(1) : 5-14
2 Hsu, K. H., H. V. Gupta, and S. Sorooshian, 1995, Artificial Neural Network Modeling of the Rainfall-Runoff Process. Water Resour. Res. 31(10) : 2517-2530   DOI
3 Jozwik, A., S. Serpico, and F. Roli, 1998, A Parallel Network of Modified 1-NN and $\kappa$-NN Classifiers Application to Remote-Sensing Images Classification. Pattern Recognition Lett., 19 : 57-62   DOI   ScienceOn
4 Kurnaz, N. M., Z. Dokur, and T. Olmez, 2005, Segmentaton of Remote-Sensing Image by Incremental Neural Network. Pattern Recognition Letters, 26 : 1096-1104   DOI   ScienceOn
5 Lillesand, T. M., and R. W. Kiefer, 1994, Remote Sensing and Image Interpretation. Third Edition, John Wiley & Sons, Inc
6 Liu, J., C. E. Goering, and L. Tian, 2001, A Neural Network for Setting Target Com Yields. Trans. ASAE 44(3) : 705-713
7 Paola, J. D., and R. A. Schowengerdt, 1995, A Detailed Comparison of Backpropagation Neural Network and Maximum-Likelihood Classifiers for Urban Land Use Classification, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 33(4) : 981-996   DOI   ScienceOn
8 Simpson, G., 1994, Crop Yield Prediction Using a CMAC Neural Network. In: Proceedings of the Society of Photo-optical Instrumentation Engineers, 2315 : 160-171
9 Villmann, T., E. Merenyi, and B. Hammer, 2003, Neural Map in Remote Sensing Image Analysis. Neural Networks, 16 : 389-403   DOI   ScienceOn
10 Hyyppa, J., H., Hyyppa, M. Inkinen, M. Engdahl, S. Linko, and Y. H. Zhu, 2000, Accuracy Comparison of Various Remote Sensing Data Sources in The Retrieval of Forest Stand Attributes. Forest Ecology and Management, 128 : 109-120   DOI   ScienceOn
11 Rissanen, J., 1978, Modeling by Short Data Description, Automation, 14 : 465-471   DOI   ScienceOn
12 Jensen, J. R., F. Qiu, and M. H. Ji, 1999, Predictive Modelling of Coniferous Forest Age Using Statistical and Artificial Neural Network Approaches Applied to Remote Sensor Data. International Journal of Remote Sensing, 20 : 2805-2822   DOI
13 Kang, M. S., M. G. Kang, S. W. Park, J. J. Lee, and K. H. Yoo, 2006a, Application of Grey Model and Artificial Neural Networks to Flood Forecasting. J. of American Water Resources Association (JAWRA). In press
14 Kang, M. S., and S. W. Park. 2003, Short-Term Flood Forecasting Using Artificial Neural Networks. J. of the Korea Society of Agricultural Engineers (KSAE) 45(2) : 45-57 (In Korean)
15 Atkinson, P. M. and A. R. L., 1997, Neural Networks in Remote Sensing. Int. J. Remote Sens. 18(4) : 699-709   DOI
16 Gomez, H., 2002, Modeling Landslide Potential in the Venezuela Andes. PhD Thesis, The University of Nottingham, UK
17 Gross, L., S. Thiria, and R. Frouin, 1999, Applying Artificial Neural Network Methodology to Ocean Color Remote Sensing. Ecological Modelling, 120 : 237-246   DOI   ScienceOn
18 Gomez, H. and T. Kavzoglu, 2005, Assesment of Shallow Landslide Susceptibility Using Artificial Neural Networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78 : 11-27   DOI   ScienceOn
19 Liu, X. H., A. K. Skimore, and H. V. Oosten, 2002, Integration of Classification Methods for Improvement of Land-Cover Map Accuracy. Photogrammetry and Remote Sensing, 56 : 257-268   DOI   ScienceOn
20 Berberoglu, S., C. D. Lloyd, P. M. Atkinson, and P. J. Curran, 2000, The integration of Special and Textural Information Using Neural Networks for Land Cover Mapping in the Mediterranean. Comput. Geosci. 26 : 385-396   DOI   ScienceOn
21 Chang, D. H. and S. Islam, 2000, Estimation of Soil Physical Properties Using Remote Sensing and Artificial Neural Network. Remote Sens. Environ., 74(3) : 534-544   DOI   ScienceOn
22 Matsuyama, T., 1987, Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing. IEEE Transactions on Geoscience and Remote Sensing GE-25(3) : 305-316   DOI   ScienceOn
23 Haykin, S., 1994, Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ: Prentice Hall
24 Del Frate, F., P. Ferrazzoli, and G. Schiavon, 2003, Retrieving Soil Moisture and Agricultureal Variables by Microwave Radiometry Using Neural Networks. Remote Sens. Environ., 84(2) : 174-183   DOI   ScienceOn
25 Ingram, J. C., T. P. Dawson, and R. J. Whittaker, 2005, Mapping Tropical Forest Structure in Southeastern Madagascar Using Remote Sensing and Artificial Neural Networks. Remote Sensing of Environment, 94 : 491-507   DOI   ScienceOn
26 Kang, M. S., S. W. Park, J. J. Lee, and K. H. Yoo. 2006b, Appling SWAT for TMDL Programs to a Small Watershed Containing Rice Paddy Fields. Agricultural Water Management 79(1) : 72-92   DOI   ScienceOn
27 Mar, J. F., 2004, Mapping Land Use/Cover in a Tropical Coastal Area using Satellite Sensor Data, GIS and Artificial Neural Networks. Estuarine Coastal and Shelf Science, 59 : 219-230   DOI   ScienceOn
28 Nash, J.E. and J. V. Sutcliffee, 1970, River Flow Forecasting through Conceptual Models, Journal of Hydrology 10 : 282-290   DOI   ScienceOn
29 Giacinto, G., F. Roli, and L. Bruzzone, 2000, Combination of Neural and Statistical Algorithms for Supervised Classification of Remote-Sensing Images. Pattern Recognition Lett., 21 : 385-397   DOI   ScienceOn
30 Keiner, L. E. and X. H. Yan, 1998, A Neral Network Model for Estimating Sea Surface Chlorophyll and Sediments from Thematic Mapper Imagery. Remote Sens. Enviorn., 66 : 153-165   DOI   ScienceOn
31 Foody, G. M., D. S. Boyd, and M. E. J. Cutler, 2003, Predictive Relations of Tropical Forest Biomass form Landsat TM Data and Their Transferability Between Regions. Remote Sensing of Environment, 85 : 463-474   DOI   ScienceOn
32 Uno, Y., S. O. Prasher, R. Lacroix, P. X. Goel, Y. Karimi, A. Viau, and R. M. Patel, 2005, Artificial Neural Networks to Predict Com Yield from Compact Airborne Spectrographic Imager Data. Computers and Electronics in Agriculture, 47 : 149-161   DOI   ScienceOn
33 Chen, Z., T. J. Feng, and Z. Houkes, 1999, Texture Segmentation Based on Wavelet and Kohonen Network for Remote Sensed Images. In: IEEE-SMC Conf., 6 : 816-821
34 Phien, H. N. and S. Sureerattanan, 2000, Neural Networks for Filtering and Forecasting of Daily and Monthly Streamflows, Hydrologic Modeling, 203-218
35 Bruzzone, L. and D. F. Prieto, 1999, An Incremental-Learning Neural Network for the Classification of Remote-Sensing Images. Pattern Recognition 20 : 1241-1248   DOI   ScienceOn
36 Kanellopoulos, I., G. G. Wilkinson, and J. Megier, 1993, Integration of Neural Network and Statistical Image Classification for Land Cover Mapping. Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS '93, Kogakuin University, Tokyo, Japan, 18-21 August
37 Zhang, Y., J. Pullianinen, S. Koponen, and M. Hallikainen, 2002, Application of an Empirical Neural Network to Surface Water Quality Estimation in the Gulf of Finland Using Combined Optical Data and Microwave Data. Remote Sens. Environ., 81(2-3) : 327-336   DOI   ScienceOn
38 Serpico, S. B., L. Bruzzone, and F. Roli, 1996, An Experimental Comparison of Neural and Statistical Non-Parametric Algorithms for Supervised Classification of Remote-Sensing Images. Pattern Lett., 17 :1331-1341   DOI   ScienceOn