A Novel Method for Unsupervised Multiple Change Detection in Hyperspectral Images Based on Binary Spectral Change Vectors

In the next years, the launch of new satellites with Hyperspectral (HS) sensors will guarantee the availability of regular multitemporal HS datasets. In order to exploit the dense sampling of the spectrum of HS sensors to discriminate multiple land-cover changes ad-hoc techniques are required. In this paper we propose a novel method for unsupervised multiple Change Detection (CD) in HS multitemporal images based on binary Spectral Change Vectors (SCVs). In greater detail, the method discriminates between unchanged and changed areas in order to focus only on the latter ones. Then, it converts the real valued SCVs in a binary form to work in a discrete high dimensional space. The binary SCVs are clustered following an hierarchical tree structure where each leaf represent a kind of change. The tree also highlights how the different changes are related among each other. The proposed approach has been tested on a multitemporal dataset acquired over an agricultural area. Experimental results confirmed that the binary SCVs allows us to detect and discriminate multiple changes by working in a simpler discrete space.