The Time Variable in Data Fusion: A Change Detection Perspective

This paper presents an overview on the image fusion concept in the context of multitemporal remote sensing image processing. In the remote sensing literature, multitemporal image analysis mainly deals with the detection of changes and land-cover transitions. Thus the paper presents and analyses the most relevant literature contributions on these topics. From the perspective of change detection and detection of land-cover transitions, multitemporal image analysis techniques can be divided into two main groups: those based on the fusion of the multitemporal information at feature level, and those based on the fusion of the multitemporal information at decision level. The former mainly exploit multitemporal image comparison techniques, which aim at highlighting the presence/absence of changes by generating change indices. These indices are then analyzed by unsupervised algorithms for extracting the change information. The latter rely mainly on classification and include both supervised and semi/partially-supervised/unsupervised methods. The paper focuses the attention on both standard (and largely used) methods and techniques proposed in the recent literature. The analysis is conducted by considering images acquired by optical and SAR systems at medium, high and very high spatial resolution.