A novel clustering method using weighted sub-sampling for an infrared search and track system

Abstract

In an infrared search and tracking (IRST) system, the clustering procedure which merges target pixels into one cluster requires larger computational load according to increasing clutters. In this paper, we propose a novel clustering method based on weighted sub-sampling to reduce clustering time and obtain suitable cluster in cluttered environment. A conventional sub-sampling method can reasonably reduce clustering time but cause large error, when obtaining cluster center. However, our proposed clustering method perform sub-sampling and assign specific weights which is the number of target pixels in sampling region to sub-sampled pixels to obtain suitable cluster center. After performing clustering procedure, the cluster center position is properly obtained using sampled pixels and their weights in the cluster. Therefore, our proposed method can not only reduce clustering time using a sub-sampling method, but also obtain proper cluster center using our proposed weights. To validate our proposed method, experimental results for several infrared and noise images are presented.