FVPNet: A Fuzzy Visual Positioning Perception Network for Small Object Segmentation in Remote Sensing Images

The increasing availability of high-resolution remote sensing images (RSIs), driven by advancements in satellite and optical imaging technologies, presents both new opportunities and challenges for small object segmentation (SOS) tasks. A primary challenge in SOS tasks is the substantial sample imbalance between foreground and background, resulting in the underrepresentation or even loss of features for small foreground targets. Furthermore, grayscale variability intensifies boundary uncertainty, complicating accurate segmentation. To address these issues, we propose a fuzzy visual perception network, which incorporates a feature inheritance downsampling (FID) module, a fuzzy refinement subnet, and a small target perception (STP) subnet, along with a hierarchical balance loss that prioritizes small foreground objects to effectively reduce foreground bias in regression. The FID module integrates diverse feature map extraction techniques to construct a more robust feature map through various subsampling methods, mitigating feature loss in foreground targets. The FR subnet employs high-order fuzzy modeling with adaptive membership functions and a constrained-space boundary adaptation strategy to address boundary uncertainty in RSIs. Meanwhile, the STP subnet adopts a “coarse-to-fine” paradigm, utilizing a pyramidal structure alongside an auxiliary semantic decoder to enhance the network’s capacity for accurate small target localization. We conduct extensive experiments and ablation studies on three benchmark datasets, iSAID, Vaihingen, and Potsdam datasets. Across the three datasets, our method achieved mean Intersection over Union scores of 73.08%, 87.37%, and 81.54%, respectively. Both quantitative and qualitative evaluations clearly demonstrate that the proposed approach significantly outperforms existing state-of-the-art methods, achieving a superior balance between segmentation accuracy and computational efficiency.