Indicizzato in scopus con codice eid=2-s2.0-62949209534 In this paper, we present a Kohonen's Self Organizing Map for the land-cover classification of multi-spectral satellite images. In order to obtain an accurate segmentation we introduced as input for the network, in addition to the spectral data, some texture measures which gives a contribution to the classification of manmade structures. The texture features were extracted from high resolution images by means of Gray Level Co-occurrence Matrix (GLCM) and standard deviation. After clustering of SOM outcomes, we associated each cluster with a major land cover and compared them with prior knowledge of the scene analyzed. The results are encouraging as showed by the high values of the accuracy.
Application of neural unsupervised methods to environmental factor analysis of multi-spectral images with texture features
GIACCO, FERDINANDO;SCARPETTA, Silvia;MARINARO, Maria;
2008-01-01
Abstract
Indicizzato in scopus con codice eid=2-s2.0-62949209534 In this paper, we present a Kohonen's Self Organizing Map for the land-cover classification of multi-spectral satellite images. In order to obtain an accurate segmentation we introduced as input for the network, in addition to the spectral data, some texture measures which gives a contribution to the classification of manmade structures. The texture features were extracted from high resolution images by means of Gray Level Co-occurrence Matrix (GLCM) and standard deviation. After clustering of SOM outcomes, we associated each cluster with a major land cover and compared them with prior knowledge of the scene analyzed. The results are encouraging as showed by the high values of the accuracy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
