A reactive N-step look-ahead scheme is used to plan paths for an unmanned air-vehicle (UAV) to maximize the probability of successfully identifying an object on the surface. UAVs are used for reconnaissance, search and rescue, and surveillance missions where the quality of camera imagery can determine the success or failure of an operation. Reactive path planning and mathematical models for calculating the Probability of Detection, Recognition, and Identification (PDRI) have been studied independently, but never merged to plan paths for UAVs. Combining these techniques results in the highest probability of successful object identification from aerial video. We utilize the Targeting Task Performance (TTP) metric to estimate the P-DRI and successfully incorporate it into a look-ahead path planner. We present both simulation and experimental results.