Topological Detection of Spatially Proximate Emitters in Spaceborne Radio Environment Maps: An Improved Persistent Homology Approach

Existing radio environment map (REM)-based emitter detection methods suffer from high false positives and missed detections in blurred or conjoined structures, or require large annotated datasets and heavy computation. This paper proposes an unsupervised method, persistent homology with agglomerative clustering (PH-AC), based on an improved persistent homology algorithm. A simulated spaceborne REM dataset is constructed via synthetic aperture passive interferometric imaging, covering isolated, adjacent-pair, and complex emitter distributions. Persistent homology tracks the birth, death, and merging of zero-dimensional connected components as the intensity threshold varies. To address missed detections for spatially proximate emitters, multidimensional topological features are constructed via feature contribution analysis. Agglomerative clustering with Ward linkage then adaptively separates emitters from noise without supervision. Experimental results show that PH-AC achieves a perfect F1 score of 1.000 in isolated scenarios; for adjacent emitters, it improves F1 by 15.7% over the best image processing method and stays within 4% of supervised deep learning methods, while requiring no annotations. In complex environments, it attains an F1 of 0.937, outperforming all compared methods. Its computational complexity is only 3.18e7 FLOPs, two orders lower than YOLO-based detectors. This work offers a lightweight, annotation-free topological paradigm for spaceborne REM emitter detection.