Mixed spectral-structural classification of very high resolution images with summation kernels

Devis Tuia; Frédéric Ratle

doi:10.1117/12.799860

9 October 2008 Mixed spectral-structural classification of very high resolution images with summation kernels

Devis Tuia, Frédéric Ratle

Proceedings Volume 7109, Image and Signal Processing for Remote Sensing XIV; 710906 (2008) https://doi.org/10.1117/12.799860
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Citation Download Citation

Devis Tuia and Frédéric Ratle "Mixed spectral-structural classification of very high resolution images with summation kernels", Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 710906 (9 October 2008); https://doi.org/10.1117/12.799860

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