Fusion of multitemporal contextual information by neural networks for multisensor remote sensing image classification

  • Authors:

  • Farid Melgani;Sebastiano B. Serpico;Gianni Vernazza

  • Affiliations:

  • Department of Biophysical and Electronic Enginneering, University of Genoa, Via Opera Pia, 11a, I-16145 Genova, Italy. Tel.: +39 010 3532752/ Fax: +39 010 3532134/ E-mail: melgani@dibe.unige.it;Department of Biophysical and Electronic Enginneering, University of Genoa, Via Opera Pia, 11a, I-16145 Genova, Italy. Tel.: +39 010 3532752/ Fax: +39 010 3532134/ E-mail: melgani@dibe.unige.it (C ...;Department of Biophysical and Electronic Enginneering, University of Genoa, Via Opera Pia, 11a, I-16145 Genova, Italy. Tel.: +39 010 3532752/ Fax: +39 010 3532134/ E-mail: melgani@dibe.unige.it

  • Venue:
  • Integrated Computer-Aided Engineering
  • Year:
  • 2003

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Abstract

The contextual analysis of a multitemporal sequence of images of a given site represents a way to improve the accuracy with respect to the non-contextual single-time classification. The proposed contextual multitemporal classification scheme consists of two stages of multilayer perceptron (MLP) neural networks for each single-time image of the multitemporal sequence. The first stage is a one-hidden layer MLP whose role is to estimate the single-time posterior probability of each class, given the feature vector. These probability estimates represent spectral information; in addition, they are utilized to generate a non-contextual classification map. The neighboring class labels of a given pixel in the non-contextual classification map are exploited to extract spatial information, while temporal information is deduced from the non-contextual maps produced by the remaining single-time images. Spatial and temporal contextual information, together with spectral information, serves as input for the second stage network where the fusion takes place. As the network configuration can influence the classification performances, three MLP-based configurations are investigated. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are presented and the performances of the proposed method are compared with those of both a classifier based on Markov random fields and a statistical contextual classifier.