Application of Self Organizing Maps to multi-temporal and multi-spectral satellite images: classification and change detection

In this paper we employ the Kohonen's Self Organizing Map (SOM) as a strategy for an unsupervised analysis of two IKONOS multispectral images of different dates. The main object is the development of an automatic multi-temporal analysis methodology of the land use modifications through change detection techniques using remotely sensed data. In order to obtain an accurate segmentation of changes we introduce as input for the network, in addition to spectral data, some texture measures, which give an essential contribution to the classification of changes in man-made structures. Furthermore we introduce a classical statistical method based on the image differencing and we evaluate the classification performances of the proposed approaches. We propose the results obtained with different combinations of the multi-temporal input data and compare them with prior knowledge of the scene analyzed.