ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume II-2/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W2, 69–74, 2015
https://doi.org/10.5194/isprsannals-II-2-W2-69-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W2, 69–74, 2015
https://doi.org/10.5194/isprsannals-II-2-W2-69-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Oct 2015

19 Oct 2015

TWO LEVELS FUSION DECISION FOR MULTISPECTRAL IMAGE PATTERN RECOGNITION

H. Elmannai1,3, M. A. Loghmari2,3, and M. S. Naceur2,3 H. Elmannai et al.
  • 1Ecole Supérieure des Communications de Tunis, Carthage University, 2083,Ariana, Tunisia
  • 2Ecole Nationale d’Ingénieurs de Tunis, Université de Tunis El Manar (UTM), 1002, Tunisia
  • 3Laboratoire de Télédétection et Système d’Informations à Référence Spatiale

Keywords: Source separation, Support Vector Machine, Feature extraction, Decisional fusion, Pattern recognition

Abstract. Major goal of multispectral data analysis is land cover classification and related applications. The dimension drawback leads to a small ratio of the remote sensing training data compared to the number of features. Therefore robust methods should be associated to overcome the dimensionality curse. The presented work proposed a pattern recognition approach. Source separation, feature extraction and decisional fusion are the main stages to establish an automatic pattern recognizer.

The first stage is pre-processing and is based on non linear source separation. The mixing process is considered non linear with gaussians distributions. The second stage performs feature extraction for Gabor, Wavelet and Curvelet transform. Feature information presentation provides an efficient information description for machine vision projects.

The third stage is a decisional fusion performed in two steps. The first step assign the best feature to each source/pattern using the accuracy matrix obtained from the learning data set. The second step is a source majority vote. Classification is performed by Support Vector Machine. Experimentation results show that the proposed fusion method enhances the classification accuracy and provide powerful tool for pattern recognition.