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

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...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-16
Hauptverfasser:	Wen, Yihan, Ma, Xianping, Zhang, Xiaokang, Pun, Man-On
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Artificial neural networks Change detection Change detection (CD) Coders Data models Deep learning denoising diffusion probabilistic model (DDPM) Detection Exploitation Feature extraction generative models Inference Machine learning Neural networks Noise reduction Probabilistic logic Probabilistic models Representations Semantics Task analysis Transformers
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