# GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM

arXiv: https://arxiv.org/abs/2306.03424 · HTML: https://mlsanity.com/paper/2306.03424/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 23 of its 71 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 23 of 23)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [CMT: Convolutional Neural Networks Meet Vision Transformers](https://mlsanity.com/paper/2107.06263/) (2021)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models](https://mlsanity.com/paper/2206.03461/) (2022)
- [Ambiguous Medical Image Segmentation using Diffusion Models](https://mlsanity.com/paper/2304.04745/) (2023)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2303.08714/) (2023)
- [RGBD2: Generative Scene Synthesis via Incremental View Inpainting using RGBD Diffusion Models](https://mlsanity.com/paper/2212.05993/) (2022)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models](https://mlsanity.com/paper/2304.05961/) (2023)
- [From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques](https://mlsanity.com/paper/2003.06583/) (2020)

## Built on by (most cited first, 21 of 21)

- [MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining](https://mlsanity.com/paper/2403.13430/) (2024)
- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation](https://mlsanity.com/paper/2504.03166/) (2025)
- [Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Models](https://mlsanity.com/paper/2503.11519/) (2025)
- [Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning](https://mlsanity.com/paper/2412.19179/) (2024)
- [Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?](https://mlsanity.com/paper/2503.07890/) (2025)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025)
- [Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models](https://mlsanity.com/paper/2412.05538/) (2024)
- [Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey](https://mlsanity.com/paper/2410.07824/) (2024)
- [UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction](https://mlsanity.com/paper/2407.11578/) (2024)
- [DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models](https://mlsanity.com/paper/2503.16426/) (2025)
- [SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection](https://mlsanity.com/paper/2404.11326/) (2024)
- [RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection](https://mlsanity.com/paper/2601.11898/) (2026)
- [COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data](https://mlsanity.com/paper/2603.03239/) (2026)
- [IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance](https://mlsanity.com/paper/2607.03696/) (2026)
- [Exploring Distortion Prior with Latent Diffusion Models for Remote Sensing Image Compression](https://mlsanity.com/paper/2406.03961/) (2024)
- [Operational Change Detection for Geographical Information: Overview and Challenges](https://mlsanity.com/paper/2503.14109/) (2025)
- [Label-frugal satellite image change detection with generative virtual exemplar learning](https://mlsanity.com/paper/2510.06926/) (2025)
- [Image augmentation with invertible networks in interactive satellite image change detection](https://mlsanity.com/paper/2510.18660/) (2025)
- [ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing](https://mlsanity.com/paper/2605.15375/) (2026)

## Abstract (arXiv)

Deep learning (DL)-based methods have recently shown great promise in bitemporal change detection (CD). Existing discriminative methods based on Convolutional Neural Networks (CNNs) and Transformers rely on discriminative representation learning for change recognition while struggling with exploring local and long-range contextual dependencies. As a result, it is still challenging to obtain fine-grained and robust CD maps in diverse ground scenes. To cope with this challenge, this work proposes a generative change detection model called GCD-DDPM to directly generate CD maps by exploiting the Denoising Diffusion Probabilistic Model (DDPM), instead of classifying each pixel into changed or unchanged categories. Furthermore, the Difference Conditional Encoder (DCE), is designed to guide the generation of CD maps by exploiting multi-level difference features. Leveraging the variational inference (VI) procedure, GCD-DDPM can adaptively re-calibrate the CD results through an iterative inference process, while accurately distinguishing subtle and irregular changes in diverse scenes. Finally, a Noise Suppression-based Semantic Enhancer (NSSE) is specifically designed to mitigate noise in the current step's change-aware feature representations from the CD Encoder. This refinement, serving as an attention map, can guide subsequent iterations while enhancing CD accuracy. Extensive experiments on four high-resolution CD datasets confirm the superior performance of the proposed GCD-DDPM. The code for this work will be available at https://github.com/udrs/GCD.