# Learn Temporal Consistency For Robust Satellite Video Detector Source: [Learn Temporal Consistency For Robust Satellite Video Detector]() Collection index: [llms.txt]() Data updated: 2026-06-17T05:00:05.807Z Authors: Weilong Guo, Shengyang Li, Yanfeng Gu Published: 2026-06-13T05:00:14.000Z ## Abstract Satellite video object detection (SVOD) for oriented and fine-grained objects plays an important role in satellite applications. Most existing SVOD methods only focus on one or a few coarse-grained categories of moving objects and represent objects with horizontal bounding boxes. They have difficulty extracting complete, accurate, and consistent information about objects in whole satellite videos. In this paper, we propose a satellite video object detection framework based on Temporal Consistency Learning (TCL). TCL adeptly detects oriented and fine-grained objects by leveraging the rich temporal contexts within satellite videos. The framework integrates three key modules: temporal and fine-grained feature aggregation (TFA), structure encoding (SE), and temporal consistency constraint (TCC). TFA and TCC modules facilitate consistent representation learning across frames, while the SE module encodes both appearance and structural information for precise fine-grained recognition. Experimental results on the SAT-MTB benchmark dataset demonstrate TCL's superior performance, achieving a new state-of-the-art oriented and fine-grained detection accuracy of 47.7% mAP--a 4.8% improvement over the baseline. Furthermore, our TCL framework readily accommodates existing image-based detectors, leading to enhanced detection accuracies. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Oriented Object Detection on SAT-MTB Our Settings Custom](): Oriented Object Detection; SAT-MTB Our Settings Custom; CA: 87.5. - [Oriented Object Detection on SAT-MTB Official](): Oriented Object Detection; SAT-MTB Official; CA: not recorded. ## Original sources and implementation [Original paper]() [PDF]()