![]() Innovative points of our model: 1) its end-to-end nature and 2) differenceĬonditional encoding. State-adaptive conditions for each sampling step, emphasizing two main Introduce dynamic difference conditional encoding to enhance step-wise regionalĪttention in DDPM for bitemporal images in CD datasets. To overcome these challenges, we propose a novel end-to-endĭDPM-based model architecture called change-aware diffusion model (CADM), whichĬan be trained using a limited annotated dataset quickly. Months of training time and a substantial volume of unlabeled remote sensingĭatasets, making it significantly more complex than generating a single-channelĬhange map. To generate intricately detailed, multi-channel remote sensing images requires (DDPMs) for training lightweight CD classifiers. Researchers have utilized pre-trained denoising diffusion probabilistic models However, their performance is limited by insufficient contextual informationĪggregation, as they struggle to fully capture the implicit contextualĭependency relationships among feature maps at different levels. Have demonstrated promising results in bitemporal change detection (CD). Download a PDF of the paper titled Change Diffusion: Change Detection Map Generation Based on Difference-Feature Guided DDPM, by Yihan Wen and 5 other authors Download PDF Abstract: Deep learning (DL) approaches based on CNN-purely or Transformer networks
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