A clustering approach to heterogeneous change detection

Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser, Robert Jenssen, Filippo Maria Bianchi, Sebastiano Serpico, Gregoire Mercier

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Change detection systems provide crucial information for damage assessment after natural disasters such as floodings, earthquakes, landslides, or to detect long-term trends in land usage, urban development, glacier dynamics, deforesta- tion, and desertification. In the last years, thanks to the development of heterogeneous or multimodal change detection methods, it was possible to re- lax the assumption of homogeneous and co-calibrated measurements. However, despite its undeniable potential, there is still a limited amount of research on het- erogeneous change detection in the fields of computer vision, pattern recognition and machine learning. I, copula theory is exploited to build local models of dependence between unchanged areas in heterogeneous images and to link their statistical distributions. In, joint distributions of heterogeneous images are obtained by transforming their marginal densities in meta-Gaussian distribu- tions, which provide simple and efficient models of multitemporal correlations. In a method based on evidence theory is proposed, which fuses cluster- ing maps of the individual heterogeneous images and then detects ”change” and ”no-change” classes, from the transition probabilities between clusters. In, the physical properties of the considered sensors and, especially, the associated measurement noise models and local joint distributions are exploited to define a ”no-change” manifold.

The capability of processing data from heterogeneous sources in the same application opens for usage of a much larger amount of information. With respect to time series, the temporal resolution can be increased and the overall time window can be extended. Nonetheless, new issues arise. Different sensors are sensitive to distinct physical conditions and comparing their measurements may produce false detections, due to inconsistencies in sensor behaviour rather than actual changes in the monitored entities. As the complexity of the fused data set increases, there could be a requirement for more flexible and complicated statistical models, which are harder to fit on data, they may be characterized by larger uncertainty in the parameter estimation and a higher computational cost. Finally, detecting and characterizing changes in heterogeneous images is not as trivial as in the homogeneous case, where a change corresponds simply to a difference in the signal values.

In this work, we propose a novel cluster-based approach for change detection in heterogeneous data. We design an unsupervised method to be as general as possible, i.e. application-independent. The proposed method processes pairs of images, acquired at different times from different sensors. In particular, one image comes from an optical sensor, whereas the second is a synthetic aperture radar (SAR) image. The images must be co-registered by a pre-processing step, to avoid that spatial misalignment of the images is misclassified as a change. Moreover, a third type of images is considered, whose elements are obtained by stacking optical and SAR images. A clustering method is executed independently on each of the three data sets. Then, the clusters identified in the first two data sets are matched against the ones from the third data set, in order to determine if the clusters from the first image split or merge in the second image. We associate changes to the occurrence of such modifications.

In this preliminary study, the problem has been defined, a possible solution has been suggested and experiments have been performed to assess the capa- bility of the proposed methodology. Making the whole process automatic is the following step, which will be treated in a further extension of this work.

Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique to detect changes, identified as clusters that split or merge in the different images. To evaluate potentials and limitations of our method, we perform experiments on real data. Preliminary results confirm the relationship between splits and merges of clusters and the occurrence of changes. However, it becomes evident that it is necessary to incorporate prior, ancillary, or application-specific information to improve the interpretation of clustering results and to identify unambiguously the areas of change.


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