A Benchmark Evaluation of Similarity Measures for Multi-temporal SAR Image Change Detection

SAR image change detection is playing an important role in various Earth Observation (EO) applications. There exist a large number of different methods that have been proposed to address this issue. However, due to the fact that several kinds of changes with diverse characteristics can arise in SAR images, there is no consensus on the their performances because most methods have been evaluated using different datasets, probably facing several kinds of changes, but without an in-depth analysis of the characteristics of SAR image changes. Therefore, two important problems arise. The first is what kind of change each approach can detect. The second is how much they can detect a kind of change. Although the importance to model any kind of changes has been realized, there is no principled methodology to carry out the analysis due to the difficulty in modeling various kinds of changes. In this paper, we propose a benchmark methodology to reach this goal by simulating selected kinds of changes in addition to using real data with changes. Six kinds of SAR changes for eight typical image categories are simulated, i.e., reflectivity changes, first order, second order, and higher order statistical changes, linear and nonlinear changes. Based on this methodology for change simulation, a comprehensive evaluation of information similarity measures is carried out. An explicit conclusion we have drawn from the evaluation is that the various methods behave very differently for all kinds of changes. We hope that this study will promote the advancement of this topic