Mitigating representation bias caused by missing pixels in methane plume detection
Systematic pixel missingness in satellite remote sensing imagery—induced by cloud cover and governed by a Missing-Not-At-Random (MNAR) mechanism—introduces representation bias in methane plume detection models, which erroneously associate “effective pixel coverage” with ground-truth labels, leading to severe false negatives under low-coverage conditions. Method: We propose a coverage-balanced weighted resampling strategy to decouple coverage from label dependence, integrated with a multimodal imputation method specifically tailored to the MNAR missingness pattern. Contribution/Results: The resulting debiased training framework preserves accuracy, recall, and F1-score while significantly reducing representation bias. On real-world low-coverage scenes, it improves plume detection rate by 23.6%. To our knowledge, this is the first work to jointly leverage coverage-aware resampling and MNAR-adapted imputation for methane remote sensing detection, thereby enhancing model robustness and generalization under complex cloud conditions.