Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) remains challenging due to complex backgrounds, low contrast, irregular object shapes, and large variations in object scale. Existing discriminative methods directly regress saliency maps, while recent diffusion-based generative approaches suffer from stochastic sampling and high computational cost. In this paper, we propose ORSIFlow, a saliency-guided rectified flow framework that reformulates ORSI-SOD as a deterministic latent flow generation problem. ORSIFlow performs saliency mask generation in a compact latent space constructed by a frozen variational autoencoder, enabling efficient inference with only a few steps. To enhance saliency awareness, we design a Salient Feature Discriminator for global semantic discrimination and a Salient Feature Calibrator for precise boundary refinement. Extensive experiments on multiple public benchmarks show that ORSIFlow achieves state-of-the-art performance with significantly improved efficiency.

翻译：光学遥感图像显著目标检测（ORSI-SOD）因复杂背景、低对比度、不规则目标形状及目标尺度剧烈变化而仍具挑战性。现有判别性方法直接回归显著图，而近期基于扩散的生成式方法则存在随机采样与高计算成本的问题。本文提出ORSIFlow——一种显著性引导的修正流框架，将ORSI-SOD重新定义为确定性潜流生成问题。ORSIFlow在由冻结变分自编码器构建的紧凑潜空间中执行显著掩码生成，仅需少量步骤即可实现高效推理。为增强显著性感知能力，我们设计了用于全局语义判别的显著特征判别器与用于精确边界校准的显著特征校准器。在多个公开基准上的大量实验表明，ORSIFlow以显著提升的效率实现了最先进的性能。