# A clustering approach to heterogeneous change detection

arXiv: https://arxiv.org/abs/1702.03176 · HTML: https://mlsanity.com/paper/1702.03176/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms](https://mlsanity.com/paper/2105.14159/) (2021)
- [Unsupervised Change Detection using DRE-CUSUM](https://mlsanity.com/paper/2201.11678/) (2022)

## Abstract (arXiv)

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.