LAUSR

Due to the integration and miniaturization trend of low-cost radar, how to realize fast synthetic aperture radar (SAR) image recognition on devices with strictly limited computational resources has become a problem worthy of attention. In this letter, we present a new SAR image recognition model based on the emerging brain-inspired hyperdimensional computing (HDC). Combined with the scattering mechanism of SAR image, we propose a new HDC encoding method called monogenic mapping, in which the monogenic feature vector generated by multiscale monogenic representations of each image is directly mapped to hypervector elements in HDC after raising its order through a simple tensor product. This method can effectively replace the conventional HDC data encoding process and solve the tough problem that existing HDC models are difficult to deal with data in the 2-D structure. Our lightweight model enables online, fast, and incremental learning of SAR image while well balancing high accuracy and efficiency. It also shows good stability under limited samples and noise corruption. Extensive experiments and comparisons with other algorithms on the MSTAR public database demonstrate the encouraging performance of our work, which provides the possibility for HDC to deploy in more scenarios.