  Hyperspectral image change detection (HSI-CD) has emerged as a crucial
research area in remote sensing due to its ability to detect subtle changes on
the earth's surface. Recently, diffusional denoising probabilistic models
(DDPM) have demonstrated remarkable performance in the generative domain. Apart
from their image generation capability, the denoising process in diffusion
models can comprehensively account for the semantic correlation of
spectral-spatial features in HSI, resulting in the retrieval of semantically
relevant features in the original image. In this work, we extend the diffusion
model's application to the HSI-CD field and propose a novel unsupervised HSI-CD
with semantic correlation diffusion model (DiffUCD). Specifically, the semantic
correlation diffusion model (SCDM) leverages abundant unlabeled samples and
fully accounts for the semantic correlation of spectral-spatial features, which
mitigates pseudo change between multi-temporal images arising from inconsistent
imaging conditions. Besides, objects with the same semantic concept at the same
spatial location may exhibit inconsistent spectral signatures at different
times, resulting in pseudo change. To address this problem, we propose a
cross-temporal contrastive learning (CTCL) mechanism that aligns the spectral
feature representations of unchanged samples. By doing so, the spectral
difference invariant features caused by environmental changes can be obtained.
Experiments conducted on three publicly available datasets demonstrate that the
proposed method outperforms the other state-of-the-art unsupervised methods in
terms of Overall Accuracy (OA), Kappa Coefficient (KC), and F1 scores,
achieving improvements of approximately 3.95%, 8.13%, and 4.45%, respectively.
Notably, our method can achieve comparable results to those fully supervised
methods requiring numerous annotated samples.
