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.
@article{arxiv.2603.28584,
title = {ORSIFlow: Saliency-Guided Rectified Flow for Optical Remote Sensing Salient Object Detection},
author = {Haojing Chen and Zhihang Liu and Yutong Li and Tao Tan and Haoyu Bian and Qiuju Ma},
journal= {arXiv preprint arXiv:2603.28584},
year = {2026}
}