Classification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks

References

• Atkinson , P. M and Tatnall , A. R. L . 1997 . Neural network in remote sensing . International Journal of Remote Sensing , 18 : 699 – 709 .
   Web of Science ®Google Scholar
• Azzali S Menenti M 1989 Irrigation water management in two Italian irrigation districts Proceedings of Workshop on Earth Net Pilot Project on Landsat TM Applications, Frascati, Italy, December 1987, pp. 41–48
   Google Scholar
• Badhwar , G. D . 1984 . Classification of corn and soybean using multi temporal thematic mapper data . Remote Sensing of Environment , 16 : 175 – 182 .
   Web of Science ®Google Scholar
• Benediktsson , J. A and Sveinsson , J. R . 1997 . Feature extraction for multi source data classification of multi source remote sensing data . IEEE Transactions on Geoscience and Remote Sensing , 18 : 727 – 740 .
   Google Scholar
• Benediktsson , A. J , Philip , H. S and Okam , K. E . 1990 . Neural network approaches versus statistical methods in classification of multi source remote sensing data . IEEE Transactions on Geoscience and Remote Sensing , 28 : 540 – 552 .
   Web of Science ®Google Scholar
• Civco , D. L . 1993 . Artificial neutral network for land cover classification and mapping . International Journal of Geographical Information System , 7 : 173 – 186 .
   Web of Science ®Google Scholar
• Conese , C and Maselli , F . 1991 . Use of multi temporal information to improve classification performance of TM scenes in complex terrain . ISPRS Journal of Photogrammetry and Remote Sensing , 46 : 187 – 197 .
   Web of Science ®Google Scholar
• Heermann , P. D and Khazenie , N . 1992 . Classification of multi spectral remote sensing data using a back-propagation neural network . IEEE Transactions on Geoscience and Remote Sensing , 30 : 80 – 88 .
   Web of Science ®Google Scholar
• Hlavaka , C. A , Haralick , R. M , Carlyle , S. M and Yokoma , R . 1980 . The discrimination of winter wheat using a growth state signature . Remote Sensing of Environment , 9 : 277 – 294 .
   Web of Science ®Google Scholar
• Jensen R. J 1986 Introductory Digital Image Processing, A Remote Sensing Perspective (New Jersey: Prentice Hall)
   Google Scholar
• Jensen , R. J , Qiu , F and Patterson , K . 2001 . A neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data . Geocarto International , 16 : 19 – 28 .
   Google Scholar
• Jones , W. P and Hoskins , J . 1987 . Back propagation, a generalized delta learning rule? . Byte , 12 : 155 – 162 .
   Google Scholar
• King T 1989 Using neural networks for pattern recognition Dr Dobb's Journal, January, 14–18
   Google Scholar
• Lambin , F. E and Strahler , H. A . 1994 . Change vector analysis in multi temporal space; a tool to detect and categorize land cover change processes using high temporal resolution satellite data . Remote Sensing of Environment , 48 : 231 – 244 .
   Web of Science ®Google Scholar
• Lillesand T. M Kiefer R. W 1987 Remote Sensing and Image Interpretation (New York: John Willy)
   Google Scholar
• Lippman , R. P . 1987 . An introduction to computing with neural nets . IEEE ASSP Magazine , 4 : 4 – 22 .
   Google Scholar
• Liu X Skidmore K. A Oosten van Henk 2000 Discrimination ability of Neural Network and Maximum Likelihood Classifiers XIX ISPRS: IAPRS, Amsterdam, The Netherlands, 16–23 July 2000, vol. XXXIII (ISPRS)
   Google Scholar
• Lo , H. C. T . 1986 . Use of multi temporal spectral profiles in agricultural land cover classification . Photogrammetric Engineering and Remote Sensing , 52 : 535 – 544 .
   Web of Science ®Google Scholar
• Maxwell K. S Hoffer M. R 1996 Mapping agricultural crops with multidate Landsat data Proceedings of ASPRS/ASCM Annual Convention Baltimore, MD, 22–25 April 1996, pp. 433–443
   Google Scholar
• Menenti M Azzali Leguizamon S Collado D. A 1986 Multi temporal analysis of Landsat Multi spectral Scanner (MSS) and Thematic Mapper (TM) data to map crops in Po valley (Italy), and in Mendoza (Argentina) Proceedings 7th International Symposium on Remote Sensing for Resource Development and Environmental Management, Enschede, The Netherlands, 25–29 August 1986, pp. 293–299
   Google Scholar
• Mohanty , K. K and Majumdar , T. J . 1996 . An Artificial Neural Network (ANN) based software package for classification of remotely sensed data . Computer and Geosciences , 22 : 81 – 87 .
   Web of Science ®Google Scholar
• Nageswara Rao , P. P and Mohan Kumar , A . 1987 . Rice crop identification and area estimation using remotely sensed data from Indian cropping patterns . International Journal of Remote Sensing , 8 : 639 – 650 .
   Web of Science ®Google Scholar
• Nunnari , G , Nucifora , A. F. M and Randieri , C . 1998 . The application of neural techniques to the modeling of the time series of atmospheric pollution data . Ecological Modeling , 111 : 187 – 205 .
   Web of Science ®Google Scholar
• Pax-Lenney and Woodcock , E. C . 1997 . Monitoring agricultural land in Egypt with multi temporal Landsat TM imagery; how many images are needed? . Remote Sensing of Environment , 59 : 522 – 529 .
   Web of Science ®Google Scholar
• Penn , B. S , Gordon , A. J and Wendlandt , R. F . 1993 . Using neural networks to locate edges and linear features in satellite images . Computer and Geosciences , 19 : 1545 – 1565 .
   Web of Science ®Google Scholar
• Richards , J. A . 1984 . Thematic mapping from multi temporal image data using principal component transformation . Remote Sensing of Environment , 16 : 35 – 46 .
   Web of Science ®Google Scholar
• Sabins F. F 1986 Remote Sensing, Principles and Interpretation (New York: Freeman)
   Google Scholar
• Swain P. H 1978 Fundamentals of pattern recognition in remote sensing In Remote sensing: The Quantitative Approach, edited by P. H. Swain and S. M. Davis (New York: McGraw-Hill), pp. 136–187
   Google Scholar
• Thiruvengadachari S Murthy C. S Raju P. V 1996 Remote sensing study in Bhakra Command area, Haryana state, India Report submitted to International Water Management Institute, Colombo, Sri Lanka
   Google Scholar
• Vieira C. A. O Mather P. M 2000 An examination of the effectiveness of multi temporal classification Proceedings of the 26th Annual Conference and Exhibition of the Remote Sensing Society, Leicester, UK, 12–14 September 2000
   Google Scholar
• Vieira C. A. O Mather P. M Tso B. C. K McCullagh M. J 1998 Using the multi-temporal, multi spectral and multi source remotely sensed data to classify agricultural crops In RSS98—Developing International Connection, Proceedings of the 24th Annual Conference and Exhibition of the Remote Sensing Society, University of Greenwich 9–11 September 1998, pp. 354–360
   Google Scholar
• Vieira C. A. O Mather P. M McCullagh M 2000 The spectral temporal response surface and its use in the multi sensor, multi temporal classification of agricultural crops XIX ISPRS: IAPRS, Amsterdam, The Netherlands 16–23 July 2000, vol. XXXIII (ISPRS)
   Google Scholar