A clustering approach to heterogeneous change detection
This addresses a challenging problem in remote sensing for earth observation, but it is incremental as it builds on existing clustering techniques without major breakthroughs.
The paper tackled change detection in heterogeneous satellite images by proposing a clustering-based method that identifies changes through cluster splits and merges, with preliminary results confirming this relationship but highlighting the need for additional information to improve interpretation.
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.