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).