Image Quality Measures for Predicting Automatic Target Recognition Performance

One important issue for Automatic Target Recognition (ATR) systems is to learn how robust the performance is under different scenarios. The quality of the input image sequence is a major factor affecting the ATR algorithm's ability to detect and recognize an object. If one can correlate the algorithm performance with different image quality measures, the recognition confidence can be predicted before applying ATR by predetermining the input image quality. In this paper, we address the utility of image quality measures and their correlations with performance failures of a principle component analysis (PCA) based ATR algorithm. Various image fusion approaches are examined to illustrate their abilities to improve ATR performance. Results show that the Shift Invariant Discrete Wavelet Transform (SiDWT) and Laplacian pyramid fusion schemes outperform other methods for improving the detection rate with the considered SAR images. Regression analysis is conducted to show that linear combinations of the selected image quality measures could explain about 60% of the variability in the non-detections of the ATR algorithm.