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Efficient SOM-Based ATR Method for SAR Imagery With Azimuth Angular Variations

The microwave imaging technique, especially for synthetic aperture radar (SAR), has significant advantages in providing high-resolution complex target images, even in darkness or adverse weather conditions. Nevertheless, it is still difficult for human operators to identify targets on SAR images because they are generated using radio signals with wavelengths at the order of cm. To deal with this, various approaches for efficient automatic target recognition (ATR), based on neural networks or support vector machines (SVM), have been developed. Previously we proposed a promising ATR method using a supervised self-organizing map (SOM), where a binarized SAR image is accurately classified by exploiting the unified distance matrix (U-matrix) metric. Although this method enhances ATR performance considerably, even with SAR images heavily contaminated by random noise, the calculation burden is enormous under expansions of scale and then cannot maintain the ATR performance, especially in cases with azimuth angle variations. In this letter, we propose a constrained learning scheme for generating the SOM and introduce the A-star algorithm to handle SOM scale expansion. Experimental investigations demonstrate the effectiveness of our proposed method.