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. Codes are available at: https://github.com/Ch3nSir/ORSIFlow.

翻译：光学遥感图像显著性目标检测（ORSI-SOD）因背景复杂、对比度低、目标形状不规则以及目标尺度差异大而仍具挑战性。现有判别式方法直接回归显著性图，而近期基于扩散的生成式方法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重构为确定性潜在流生成问题。ORSIFlow在由冻结变分自编码器构建的紧凑潜在空间中生成显著性掩码，仅需少量步骤即可实现高效推理。为增强显著性感知，我们设计了用于全局语义辨别的显著性特征判别器，以及用于精确边界校准的显著性特征校准器。在多个公开基准上的大量实验表明，ORSIFlow在显著提升效率的同时达到了最先进的性能。代码请见：https://github.com/Ch3nSir/ORSIFlow。