|Title||Improving active learning methods using spatial information|
|Author(s)||Pasolli, Edoardo; Melgani, Farid; Tuia, Devis; Pacifici, Fabio; Emery, William J.|
|Source||In: 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings. - - p. 3923 - 3926.|
|Event||2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, 2011-07-24/2011-07-29|
|Publication type||Contribution in proceedings|
|Keyword(s)||Active learning - spatial information - support vector machines (SVMs) - very-high-resolution (VHR) images|
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning.