Segmentation is useful during sub-pixel target detection in hyperspectral data. Our standard algorithm uses estimates of the actual pixel being examined (based on surrounding pixels) and of the covariance matrix of the background (traditionally based on all the pixels in the image). Previous works have showed that improving performance in sub-pixel target detection can be achieved by making better estimates of the covariance matrix by using segmentation. One of the challenges is that pixel assignment to its segment can be influenced by the presence of the target that will lead us to miss the target. Therefore, it is desirable to assign the examined pixel by using the neighbors of the pixel assignment without involving the pixel itself is needed. We developed a new reassignment segmentation without involving the central pixel. Using simulations and several analytical tools, we analyzed the matched-filter algorithm, both with and without segmentation, and compare performances of the receiver operating characteristic curves. Our algorithm showed better receiver operating characteristic curves in low false positive rate in the range 0-0.01 (the operating range of our applications), i.e., we got a higher true positive rate for the same false positive rate.