A Self-structuring Data Learning Algorithm was introduced and has been implemented in our prior work. While the algorithm and the software package are advancing, it has been tested with both synthetic data and real-world data. After encouraging synthetic data test results, real-world data testing also shows promising outcomes while posing some challenges such as object occlusion, objects merging, and going into and emerging from under bridge. To resolve such problems, a multi-int solution is proposed. One of the key features in this solution is similarity measure. There are different types of similarity measures. In this paper, we primarily focus on aerial images similarity measure. The images we worked on presents unique challenge in similarity measure because of small object in distance and large area image, which consequently provides limited information. To deal with this difficulty, we have developed 14 different similarity metrics by employing Normalized Cross Correlation method, Sum of Squared Differences, and overlapping and colors of pixels. We used object tracking ability to evaluate the metrics. The simulation results show each metric has some advantages and disadvantages. In attempt to improve tracking capability, we imposed some metrics thresholds in addition to the image similarity metrics. Such metrics thresholds were learned from labeled data with valuation of tracking correctness. To further enhance tracking ability, speed similarity was incorporated on top of two features mentioned above. More improvement can be done by studying robustness of images similarity metrics and using tracks fusion.