To perform point target acquisition in multispectral and hyperspectral images, it is often advantageous to compare the signature of the investigated pixel to a known target signature. To do this properly, it is necessary to estimate the expected mean and covariance matrix of an investigated pixel in a particular location, based on its local surroundings. The degree to which this pixel signature differs from the estimated background then becomes the data, which is matched to the desired target signature. The standard method for such an analysis is the RX algorithm of Reed and Yu. The mean is normally estimated from the local environment of the pixel; the covariance matrix can either be estimated globally or in some local window. In recent research, we have considered how to improve the algorithm by eliminating edge points as potential false alarms. In the present work, a prior segmentation of the image before processing is utilized. While our estimate for the mean continues to be based on the immediate neighbors of the investigated pixel, our estimate of the covariance matrix is now based on the covariance matrix of the segment to which the adjacent pixels belong. In this way, we get a more accurate estimate of the covariance matrix. Results on real multispectral and hyperspectral images with embedded targets in several spectral regions are presented and improvement is demonstrated.