Figure 6
Overview of our proposed DiffSpectralNet (a) unsupervised spectral–spatial feature learning network. \(x_0\) and \(x_T\) represent HSI patches of timestep \(0\) and timestep \(T\). \(q(x_t \mid x_{t-1})\) and \(p(x_{t-1} \mid x_{t})\) represent forward and reverse spectral–spatial diffusion processes, respectively. (b) Supervised classification \((1)\) extracting hierarchical features from the pretrained denoising U-Net decoder in terms of different timestep t. \((2)\) Using the patch-wise feature vectors to train an cross-layer transformer for HSI classification.
