A competitive pixel-object approach for land cover classification

This paper describes a novel remote sensing land cover classification approach named competitive pixel-object classification, based on Bayesian neural networks and image segmentation. This approach makes use of both pixel spectral features and object features resulting from image segmentation through a competitive mechanism to resolve the problem of spectral confusion caused by reflectance similarity of some land cover types that traditional pixel-based classification cannot resolve. The competitive pixel-object method reduces the unreliability of object feature information produced by over- or under-segmentation of the image through a competitive mechanism. The experiment shows that the competitive pixel-object approach produces higher classification accuracy than either pixel-based classification or object-oriented classification.

[1]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[2]  Geoffrey E. Hinton.  Learning Translation Invariant Recognition in Massively Parallel Networks , 1987, PARLE.

[3]  D. Civco,et al.  IMPERVIOUS SURFACE MAPPING FOR THE STATE OF CONNECTICUT 1 , 1997 .

[4]  M. Baatz,et al.  Object-oriented and Multi-scale Image Analysis in Semantic Networks Introduction: the Necessity for Integration of Remote Sensing and Gis , 2022 .

[5]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[6]  Horst Bischof,et al.  Multispectral classification of Landsat-images using neural networks , 1992, IEEE Trans. Geosci. Remote. Sens..

[7]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[8]  J. Townshend,et al.  Beware of per-pixel characterization of land cover , 2000 .

[9]  S. Kaya,et al.  The Use of Radar Remote Sensing for Identifying Environmental Factors Associated with Malaria Risk in Coastal Kenya , 2002 .

[10]  Mingjun Song.  Hybrid pixel-object pattern recognition in remote sensing , 2002 .

[11]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[12]  Robert A. Schowengerdt,et al.  A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .

[13]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[14]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[15]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[16]  David J. C. MacKay,et al.  The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.

[17]  Daniel L. Civco,et al.  Artificial Neural Networks for Land-Cover Classification and Mapping , 1993, Int. J. Geogr. Inf. Sci..

[18]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[19]  T. Bauer,et al.  Per-parcel land use classification in urban areas applying a rule-based technique , 2001 .

[20]  D. Civco,et al.  A COMPARISON OF LAND USE AND LAND COVER CHANGE DETECTION METHODS , 2002 .