Application of Neural Unsupervised Methods to Environmental Factor Analysis of Multi-Spectral Images with Texture Features

F. Giacco, S. Scarpetta, M. Marinaro, and L. Pugliese (Italy)


Self-organizing map, remote sensing, image analysis, K means clustering, gray level co-occurrence matrix.


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 in troduced 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 fea tures were extracted from high resolution images by means of Gray Level Co-occurrence Matrix (GLCM) and standard deviation. After clustering of SOM outcomes, we associ ated each cluster with a major land cover and compared them with prior knowledge of the scene analyzed. The re sults are encouraging as showed by the high values of the accuracy.

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