Airborne LiDAR has become a kind of indispensable measurement device in the current field of remote sensing. However, target extraction using traditional airborne LiDAR based on single-point scanning requires filtering and point cloud segmentation operations, which are complicated and time consuming. Although some researchers have studied streak tube imaging LiDAR (STIL) before, there are few reports in which it is used as an airborne LiDAR for ground measurement in large-scale field. We propose a method of ground target extraction using STIL. Taking advantage of the structural properties of the STIL, complex filtering and point cloud segmentation algorithms are avoided in the target extraction method. The purpose of this article is to verify the feasibility of airborne STIL in ground target extraction. We analyzed the raw streak signal image collected by field experiment and used morphology and intensity information to extract features. After that, we employed the decision tree classifier to classify the four kinds of targets and evaluated the extraction results. The results show that the target extraction achieved satisfactory consequences under an acceptable level. That demonstrates that ground target extraction using STIL is feasible in the field of large-scale remote sensing.