Nowadays targets and objects matching and tracking in sequences of images play an important role in various areas including military industry particularly in air systems. Targets in these applications can be fixed or mobile. Target matching algorithm shall keep the ability of tracking desired target (reference target) in sequence of image despite of changes in type of camera, viewing angle, distance, environmental conditions and camera parameters like resolution and etc. The Target matching algorithm can be divided into two parts. The first part includes finding features of received and reference images and second part include describing features matching of two images. In the first part we use a scale space in order to stabilize detected features to the scale changes. In the second part we attribute feature points that we obtained in the first part, a description using brightness value around the feature. In this thesis target detection using features have been investigated. First we review well-known BRISK and SAIFT algorithm then we propose a new algorithm based on these two. The new algorithm uses directional pattern to describe the feature. The direction of this paper is perpendicular to the angle of the feature. This provides more useful information about lights around the feature for making descriptor vector. Furthermore in the proposed algorithm, the output vector consists of multilevel values instead of binary values. Levels of output vectors can be adjusted using a single parameter so that the processor with low computing ability could convert the output vector to a four-level vector. Finally we evaluate and compare these two algorithms with BRISK algorithm. The results of this thesis show that the discriminatory power and stability of the proposed algorithm is more efficient than BRISK algorithm and the efficiency of the algorithm is about the same as BRISK algorithm