Mamba-Driven Diffusion Model for Salient Object Detection in Optical Remote Sensing Images

Existing Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) methods mainly rely on a semantic segmentation paradigm, which relies on pixel-wise probabilities, leading to overconfident mispredictions. In contrast, the random sampling process of the diffusion model allows multiple possible predictions to be drawn from the mask distribution, effectively alleviating this problem. However, existing diffusion models mainly use Transformers as conditional feature extraction networks. Although they are good at global modeling, they have limited ability to handle long-range dependencies due to computational complexity. To overcome these challenges, we introduce MambaDif, an innovative diffusion model architecture based on Mamba. Specifically, we regard ORSI-SOD as a conditional mask generation task leveraging the diffusion model and achieving target distribution matching by adding noise to the mask and iteratively denoising it to match the target distribution. Then, we adopt Mamba to extract global features, efficiently process long sequences, and capture global contextual information with linear complexity. In addition, we introduce the global-local feature collaborative completion module (GLM), which combines the ability of convolutional layers to extract local features with the advantage of Mamba in capturing long-range dependencies, thereby achieving excellent denoising performance. Extensive experiments show that MambaDif outperforms SOTA methods in eight evaluation metrics on two standard datasets (EORSSD and ORSSD). We also report the generalization performance of the model on the challenging ORSI-4199 to evaluate its robustness.