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. © 2011 IEEE.
Improving active learning methods using spatial information / Pasolli, E.; Melgani, F.; Tuia, D.; Pacifici, F.; Emery, W. J.. - (2011), pp. 3923-3926. (Intervento presentato al convegno 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 tenutosi a Vancouver, BC, can nel 2011) [10.1109/IGARSS.2011.6050089].
Improving active learning methods using spatial information
Pasolli E.;
2011
Abstract
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. © 2011 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
