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Mapping localized spectral features in complex scenes demands sensitive and robust detection algorithms. This letter investigates two aspects of large images that can harm matched filter (MF) detection performance. First, multimodal backgrounds may violate normality assumptions. Second, outlier features can trigger false detections due to large projections onto the target vector. We review two state-of-the-art methods designed to resolve these issues. The background clustering of Funk models multimodal backgrounds, and the mixture-tuned (MT) MF of Boardman and Kruse addresses outliers. We demonstrate that combining the two methods has additional performance benefits. An MT cluster MF shows effective performance on simulated and airborne data sets. We demonstrate target detection scenarios that evidence multimodality, outliers, and their combination. These experiments explore the performance of the component algorithms and the practical circumstances that can favor a combined approach.