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Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation

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  • S. Gandhimathi Alias Usha
  • S Vasuki

Abstract

Change detection in remote sensing images provides useful information for various applications. This paper proposes a robust methodology for the analysis of multispectral imagery using Deep belief network (DBN) and Fuzzy interference system (FIS). Initially Euclidean distance and cosine angle distance features are extracted from the image. Deep learning is a robust machine learning method in which the extracted features are processed through set linear mapping and the changes are detected. However, the coarse spatial resolution indicating the intensity of modifications in class proportion instead of accounting for the change using discrete land covers classes is used in fuzzy image classification. Hence, the FIS is combined with DBN which allows defining our own rules to detect the changes accurately. It uses triangular membership function to plot the changes. The experimental results show that the proposed method enhanced the change detection by improving the performance parameters.

Suggested Citation

  • S. Gandhimathi Alias Usha & S Vasuki, 2017. "Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 7(6), pages 206-213.
  • Handle: RePEc:asi:joasrj:v:7:y:2017:i:6:p:206-213:id:3817
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