Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations

TL;DR

MaSoN is an unsupervised remote sensing change detection framework that synthesizes diverse, data-driven changes in latent space, achieving state-of-the-art results across multiple benchmarks without relying on predefined change assumptions.

Contribution

It introduces a novel latent space perturbation method for unsupervised change detection, enabling flexible, data-driven change synthesis that generalizes well across various scenarios.

Findings

Achieves state-of-the-art performance on five benchmarks.

Improves average F1 score by 14.1 percentage points.

Effectively extends to new modalities like SAR.

Abstract

Unsupervised change detection (UCD) in remote sensing aims to localise semantic changes between two images of the same region without relying on labelled data during training. Most recent approaches rely either on frozen foundation models in a training-free manner or on training with synthetic changes generated in pixel space. Both strategies inherently rely on predefined assumptions about change types, typically introduced through handcrafted rules, external datasets, or auxiliary generative models. Due to these assumptions, such methods fail to generalise beyond a few change types, limiting their real-world usage, especially in rare or complex scenarios. To address this, we propose MaSoN (Make Some Noise), an end-to-end UCD framework that synthesises diverse changes directly in the latent feature space during training. It generates changes that are dynamically estimated using feature statistics of target data, enabling diverse yet data-driven variation aligned with the target domain. It also easily extends to new modalities, such as SAR. MaSoN generalises strongly across diverse change types and achieves state-of-the-art performance on five benchmarks, improving the average F1 score by 14.1 percentage points. Project page: https://blaz-r.github.io/mason_ucd