Estimating per‐pixel thematic uncertainty in remote sensing classifications

References

• Aires , F. , Prigent , C. and Rossow , W. B. 2004 . Neural network uncertainty assessment using Bayesian statistics: a remote sensing application. . Neural Computation , 16 : 2415 – 2458 .
   PubMedGoogle Scholar
• Ashish , D. , Hoogenboom , G. and McClendon , R. W. 2004 . Land‐use classification of gray‐scale aerial images using probabilistic neural networks. . Transactions of the American Society of Agricultural Engineers , 47 : 1813 – 1819 .
   Google Scholar
• Atkinson , P. M. and Foody , G. M. 2002 . “ Uncertainty in remote sensing and GIS: fundamentals. ” . In Uncertainty in Remote Sensing and GIS , Edited by: Foody , G. M and Atkinson , P. M . 1 – 18 . Chichester : Wiley .
   Google Scholar
• Atkinson , P. M. and Tatnall , A. R. L. 1997 . Neural networks in remote sensing. . International Journal of Remote Sensing , 18 : 699 – 709 .
   Web of Science ®Google Scholar
• Benediktsson , J. A. , Swain , P. H. and Ersoy , O. K. 1990 . Neural network approaches versus statistical methods in classification of multisource remote sensing data. . IEEE Transactions on Geoscience and Remote Sensing , 28 : 540 – 551 .
   Web of Science ®Google Scholar
• Bishop , C. M. 1995 . Neural Networks for Pattern Recognition , Oxford : Oxford University Press .
   Google Scholar
• Brown , K. M. 2005 . “ Per‐pixel uncertainty for change detection using airborne sensor data. ” . In GeoDynamics , Edited by: Atkinson , P. M , Foody , G. M , Darby , S. E and Wu , F . 103 – 118 . Boca Raton : CRC Press .
   Google Scholar
• Brown , K. M. , Foody , G. M. and Atkinson , P. M. 2006 . “ Deriving thematic uncertainty measures in remote sensing using classification outputs. ” . In Proceedings of 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences , Edited by: Caetano , M and Painho , M . 543 – 552 . Lisbon : IGP . 5–7 July 2006, Lisbon, Portugal
   Google Scholar
• Brown , K. , Duncan , A. , O'Dwyer , C. , Davison , B. , Hogarth , P. , Butler , D. and Sampson , E. 2003 . “ Integrated airborne data collection by the Environment Agency. ” . In Proceedings of Annual Conference of the Remote Sensing and Photogrammetry Society 10–12 September 2003, Nottingham. CD‐ROM, RSPSoc, Nottingham
   Google Scholar
• Campbell , J. B. 1981 . Spatial correlation effects upon accuracy of supervised classification of land cover. . Photogrammetric Engineering and Remote Sensing , 47 : 355 – 363 .
   Web of Science ®Google Scholar
• Carmel , Y. , Dean , D. J. and Flather , C. H. 2001 . Combining location and classification error sources for estimating multi‐temporal database accuracy. . Photogrammetric Engineering and Remote Sensing , 67 : 865 – 872 .
   Web of Science ®Google Scholar
• Comber , A. , Fisher , P. and Wadsworth , R. 2004 . Assessment of a semantic statistical approach to detecting land cover change using inconsistent data sets. . Photogrammetric Engineering and Remote Sensing , 70 : 931 – 938 .
   Web of Science ®Google Scholar
• Crosetto , M. and Tarantola , S. 2001 . Uncertainty and sensitivity analysis: tools for GIS‐based model implementation. . International Journal of Geographical Information Science , 15 : 415 – 437 .
   Web of Science ®Google Scholar
• Foody , G. M. 2000 . Mapping land cover from remotely sensed data with a softened feedforward neural network classification. . Journal of Intelligent and Robotic Systems , 29 : 433 – 449 .
   Web of Science ®Google Scholar
• Foody , G. M. 2005 . Local characterization of thematic classification accuracy spatially constrained confusion matrices. . International Journal of Remote Sensing , 26 : 1217 – 1228 .
   Web of Science ®Google Scholar
• Foody , G. M. and Arora , M. K. 1997 . An evaluation of some factors affecting the accuracy of classification by an artificial neural network. . International Journal of Remote Sensing , 18 : 799 – 810 .
   Web of Science ®Google Scholar
• Foody , G. M. , Campbell , N. A. , Trodd , N. M. and Wood , T. F. 1992 . Derivation and application of probabilistic statistics of class membership from maximum‐likelihood classification. . Photogrammetric Engineering and Remote Sensing , 58 : 1335 – 1341 .
   Web of Science ®Google Scholar
• Gahegan , M. and Ehlers , M. 2000 . A framework for the modelling of uncertainty between remote sensing and geographic information systems. . ISPRS Journal of Photogrammetry and Remote Sensing , 55 : 176 – 188 .
   Web of Science ®Google Scholar
• Gong , P. , Pu , R. and Chen , J. 1996 . Mapping ecological land systems and classification uncertainties from digital elevation and forest‐cover data using neural networks. . Photogrammetric Engineering and Remote Sensing , 62 : 1249 – 1260 .
   Web of Science ®Google Scholar
• Goodchild , M and Gopal , S , eds. 1989 . Accuracy of Spatial Databases , London : Taylor and Francis .
   Google Scholar
• Hart , S. J. , Shaffer , R. E. , Rose‐Pehrsson , S. L. and McDonald , J. R. 2001 . Using physics‐based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys. . IEEE Transactions on Geoscience and Remote Sensing , 39 : 797 – 804 .
   Google Scholar
• Heuvelink , G. B. M. and Pebesma , E. J. 1999 . Spatial aggregation and soil process modelling. . Geoderma , 89 : 47 – 65 .
   Web of Science ®Google Scholar
• Janssen , L. L. F. and van der Wel , F. J. M. 1994 . Accuracy assessment of satellite derived land‐cover data: a review. . Photogrammetric Engineering and Remote Sensing , 60 : 419 – 426 .
   Web of Science ®Google Scholar
• Kanellopoulos , I. and Wilkinson , G. G. 1997 . Strategies and best practice for neural network image classification. . International Journal of Remote Sensing , 18 : 711 – 725 .
   Web of Science ®Google Scholar
• Kavzoglu , T. and Mather , P. M. 1999 . Pruning artificial neural networks: an example using land cover classification of multi‐sensor images. . International Journal of Remote Sensing , 20 : 2787 – 2803 .
   Web of Science ®Google Scholar
• Kavzoglu , T. and Mather , P. M. 2003 . The use of backpropagating artificial neural networks in land cover classification. . International Journal of Remote Sensing , 24 : 4907 – 4938 .
   Web of Science ®Google Scholar
• Keramitsoglou , I. , Sarimveis , H. , Kiranoudis , C. T. , Kontoes , C. , Sifakis , N. and Fitoka , E. 2006 . The performance of pixel window algorithms in the classification of habitats using VHSR imagery. . ISPRS Journal of Photogrammetry and Remote Sensing , 60 : 225 – 238 .
   Web of Science ®Google Scholar
• Liu , W. G. , Gopal , S. and Woodcock , C. E. 2004 . Uncertainty and confidence in land cover classification using a hybrid classifier approach. . Photogrammetric Engineering and Remote Sensing , 70 : 963 – 971 .
   Web of Science ®Google Scholar
• Mahapatra , A. , Ramchandran , R. and Krishnan , R. 2004 . Modelling the uncertainty in orientation of IRS‐1C/1D with a rigorous photogrammetric model. . Photogrammetric Engineering and Remote Sensing , 70 : 939 – 946 .
   Google Scholar
• Maselli , F. , Conese , C. and Petkov , L. 1994 . Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. . ISPRS Journal of Photogrammetry and Remote Sensing , 49 : 13 – 20 .
   Web of Science ®Google Scholar
• Paola , J. D. and Schowengerdt , R. A. 1997 . The effect of neural‐network structure on a multispectral land‐use/land‐cover classification. . Photogrammetric Engineering and Remote Sensing , 63 : 535 – 544 .
   Web of Science ®Google Scholar
• Pontius , R. G. 2000 . Quantification error versus location error in comparison of categorical maps. . Photogrammetric Engineering and Remote Sensing , 66 : 1011 – 1016 .
   Web of Science ®Google Scholar
• Raghu , P. P. and Yegnanarayana , B. 1998 . Supervised texture classification using a probabilistic neural network and constraint satisfaction model. . IEEE Transactions on Neural Networks , 9 : 516 – 522 .
   PubMed Web of Science ®Google Scholar
• Richards , J. A. 2005 . Analysis of remotely sensed data: the formative decades and the future. . IEEE Transactions on Geoscience and Remote Sensing , 43 : 422 – 432 .
   Web of Science ®Google Scholar
• Specht , D. F. 1990 . Probabilistic neural networks. . Neural Networks , 3 : 109 – 118 .
   Web of Science ®Google Scholar
• Tian , B. and Azimi‐Sadjadi , M. R. 2001 . Comparison of two different PNN training approaches for satellite cloud data classification. . IEEE Transactions on Neural Networks , 12 : 164 – 168 .
   PubMed Web of Science ®Google Scholar
• Wang , J. Q. , Azimi‐Sadjadi , M. R. and Reinke , D. 2004 . A temporally adaptive classifier for multispectral imagery. . IEEE Transactions on Neural Networks , 15 : 159 – 165 .
   PubMedGoogle Scholar
• WS Atkins . 2004 . Ainsdale Sand Dunes National Nature Reserve Environmental Impact Assessment of Options for Management of Seaward Areas , Warrington : WS Atkins .
   Google Scholar