Matched filter, which models background variability using the statistics of the entire image with the assumption of rare and small targets, often fails when the target materials are frequently present in the image data. In this study, an iterative matched filtering technique is proposed which can effectively reduce the contamination of background statistics by target signal without any complicated spectral or spatial pre-processing. It applies matched filter iteratively with gradual exclusion of target-like pixels from background characterization based on the matched filtered score. Experimental results using the real airborne hyperspectral image data and simulated data with artificial mineral targets show that the proposed method can dramatically improve the detection performance. Though the statistical complexity of background materials is not investigated, it is expected to be used as a simple and practical technique for improving the detection performance of matched filter by reducing target leakage effect when the target materials are frequently present in the image data. This technique also can be directly adopted by other extensions of matched filters such as constrained energy minimization (CEM) and adaptive cosine estimator (ACE).