Neural network-based clustering for agriculture management

Remote sensing images have been used productively for land cover identification to accurately manage and control agricultural and environmental resources. However, these images have often been interpreted interactively due to the lack of effective automated methods. We propose such a method using self-organizing maps (SOM) based spectral clustering, for agriculture management. By combining the powerful aspects of the SOM (adaptive vector quantization in a topology preserving manner) and of the spectral clustering (a manifold learning based on eigendecomposition of pairwise similarities), the proposed method achieves successful results, as shown on three study areas with images from RapidEye (a recent constellation of satellites with a specific focus on agricultural applications).

• Publication year

  2012

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  2012-09-18

• Fields of study

  Agricultural and Food Sciences, Computer Science

• Identifiers
  DOI 10.1186/1687-6180-2012-200
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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  2007influential reference
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  1995influential reference
• Self-Organizing Maps

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  1994cited by this paper
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  1993influential reference

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