Accurate and robust techniques for automated feature extraction (AFE) from remotely-sensed imagery are an important area of research, having many applications in the civilian and military/intelligence arenas. Much work has been undertaken in developing sophisticated tools for performing these tasks. However, while many of these tools have been shown to perform quite well (such as the GENIE and Genie Pro software developed at LANL), these tools are not perfect. The classification algorithms produced often have significant errors, such as false-alarms and missed detections. We describe some efforts at improving this situation in which we add a clutter mitigation layer to our existing AFE software (Genie Pro). This clutter mitigation layer takes as input the output from the previous feature extraction (classification) layer and, using the same training data (pixels providing examples of the classes of interest), uses similar machine-learning techniques to those used in the previous AFE layer to optimise an image-processing pipeline aimed at improving any errors existing in the AFE output. While the AFE layer optimises an image processing pipeline that can combine spectral, logical, textural, morphological and other spatial operators, etc., the clutter mitigation layer is limited to a pool of morphological operators. The resulting clutter mitigation algorithm will not only be optimized for the particular feature of interest but will also be co-optimized with the preceding feature extraction algorithm. We demonstrate these techniques on several feature extraction problems in various multi-spectral, remotely-sensed images.