A NOVEL CHANGE DETECTION METHOD FOR MULTITEMPORAL HYPERSPECTRAL IMAGES BASED ON A DISCRETE REPRESENTATION OF THE CHANGE INFORMATION

Multitemporal Hyperspectral (HS) images can be used in Change Detection (CD) to identify and discriminate among different kinds of change due to the fine sampling of the spectrum by HS sensors. In this work we propose a novel method for unsupervised multiple CD in multitemporal HS data based on binary Spectral Change Vectors (SCVs) and an agglomerative hierarchical clustering. First, we perform binary CD to separate changed from unchanged pixels. Second, we convert the real valued SCVs into binary ones. Thus we move from a real valued high dimensional space to a discrete one. The binary signatures are used to construct a dendrogram following an hierarchical agglomerative clustering approach. Finally, we exploit the hierarchical structure to discriminate among the kinds of change in a fully unsupervised manner. The experimental results obtained on the real dataset confirmed the effectiveness of the proposed method.