# A clustering approach to heterogeneous change detection

## TL;DR

This paper introduces a clustering-based method for detecting changes in heterogeneous satellite images from different sensors over time, highlighting its potential and limitations through real data experiments.

## Contribution

It proposes a novel clustering approach to identify changes in heterogeneous multitemporal satellite images, addressing a less-studied challenge in remote sensing.

## Key findings

- Clusters splitting or merging correlates with changes.
- Preliminary results show potential but need additional information for clarity.
- Method works on real data but requires further refinement.

## Abstract

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.