A neural-statistical approach to multitemporal and multisource remote-sensing image classification

A data fusion approach to the classification of multisource and multitemporal remote-sensing images is proposed. The method is based on the application of the Bayes rule for minimum error to the "compound" classification of pairs of multisource images acquired at two different dates. In particular, the fusion of multisource data is obtained by using multilayer perceptron neural networks for a nonparametric estimation of posterior class probabilities. The temporal correlation between images is taken into account by the prior joint probabilities of classes at the two dates. As a novel contribution of this paper, such joint probabilities are automatically estimated by applying a specific formulation of the expectation-maximization (EM) algorithm to the data to be classified. Experiments carried out on a multisource and multitemporal data set confirmed the effectiveness of the proposed approach.

[1]  Alan H. Strahler,et al.  The Use of Prior Probabilities in Maximum Likelihood Classification , 1980 .

[2]  David Landgrebe,et al.  Utilizing Multitemporal Data by a Stochastic Model , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[3]  John A. Richards,et al.  Multisource Data Analysis in Remote Sensing and Geographic Information Processing , 1985 .

[4]  Isabelle Bloch,et al.  Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing , 1997, IEEE Trans. Geosci. Remote. Sens..

[5]  Jon Atli Benediktsson,et al.  a Method of Statistical Multisource Classification with a Mechanism to We!ght the Influence of the Data Sources , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[6]  Anil K. Jain,et al.  A Markov random field model for classification of multisource satellite imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[7]  P. H. Swain,et al.  Bayesian classification in a time-varying environment , 1978 .

[8]  Philip H. Swain,et al.  Improving classification of crop residues using digital land ownership data and Landsat TM imagery , 1991 .

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

[10]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[11]  N. Khazenie,et al.  Spatial-temporal Autocorrelated Model For Contextual Classification , 1990 .

[12]  Sebastiano B. Serpico,et al.  Classification of multisensor remote-sensing images by structured neural networks , 1995, IEEE Trans. Geosci. Remote. Sens..

[13]  Steven E. Franklin,et al.  Ancillary data input to satellite remote sensing of complex terrain phenomena , 1989 .

[14]  D.A. Landgrebe,et al.  Classification with spatio-temporal interpixel class dependency contexts , 1992, IEEE Trans. Geosci. Remote. Sens..

[15]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[16]  F. Roli,et al.  Multisource Classification of Complex Rural Areas by Statistical and Neural-Network Approaches , 1997 .

[17]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[18]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[19]  L. Janssen,et al.  Implementation of temporal relationships in knowledge based classification of satellite images. , 1991 .

[20]  Tong Lee,et al.  Probabilistic and Evidential Approaches for Multisource Data Analysis , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[21]  D. Leckie.  Synergism of synthetic aperture radar and visible/infrared data for forest type discrimination. , 1990 .

[22]  H. Gish,et al.  A probabilistic approach to the understanding and training of neural network classifiers , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[23]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[24]  M. Goldberg,et al.  A hierarchical expert system for updating forestry maps with Landsat data , 1985, Proceedings of the IEEE.

[25]  Peng Gong.  Integrated Analysis of Spatial Data from Multiple Sources: An Overview , 1994 .

[26]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[27]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[28]  David A. Landgrebe,et al.  A means for utilizing ancillary information in multispectral classification. [of remotely sensed data , 1982 .

[29]  Anil K. Jain,et al.  Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[30]  Lorenzo Bruzzone,et al.  An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images , 1996, Pattern Recognit. Lett..

[31]  P. Swain,et al.  A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data , 1990 .

[32]  Jean-Paul Rasson,et al.  Multivariate Discriminant Analysis and Maximum Penalized Likelihood Density Estimation , 1995 .

[33]  T. Logan,et al.  Improving forest cover classification accuracy from Landsat by incorporating topographic information , 1978 .

[34]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[35]  Lorenzo Bruzzone,et al.  An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images , 1997, IEEE Trans. Geosci. Remote. Sens..

[36]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[37]  D. Peddle,et al.  Image texture processing and data integration for surface pattern discrimination , 1991 .