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Recognition and Identification of Targets in Aerial images play a significant role and wide applications in imaging guidance of cruise missiles, standoff missiles and during terminal phase of ballistic missiles from optics images or radar images. A proposed scene matching algorithm based on both feature detection and extraction Speeded-Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) techniques. The extracted features are invariant to image scaling, translation, rotation, and partially invariant to illumination changes and affine or 3D projection. Image data set references of target are collected based on the criteria of azimuth, elevation and altitude of the target image region of interest. The proposed targets are assumed to be buildings. Firstly, SURF and MSER local feature vectors are detected and extracted from reference images of the target and merged to constitute the feature vectors matrix offline. Secondly, a feature detection and extraction using both SURF and MSER algorithms for the real time captured image are performed to form real time feature matrix. Finally, a matching algorithm is applied between both matrices of reference images and real time image. Moreover, Key points outlier elimination technique is used based on a Random Sample Consensus (RANSAC) algorithm to reduce false matching and speed up the matching procedure. The proposed algorithm has been tested on different real image data sets. A comparative analysis is performed between the SURF, FAST corner detector and MSER individually with the proposed SURF-MSER algorithm. The proposed algorithm process more complicated deformation between reference image and real time acquired image and attained higher matching accuracy.

Automatic Aerial Target Recognition using A Robust SURF-MSER Feature-based Algorithm