Joint super-resolution and classification of radar images using convolutional neural networks

Deep learning methodologies are extensively applied in addressing two-dimensional (2D) and three-dimensional (3D) computer vision challenges, encompassing tasks like object detection, super-resolution (SR), and classification. Radar imagery, however, contends with lower resolution compared to optical counterparts, posing a formidable obstacle in developing accurate computer vision models, particularly classifiers. This limitation stems from the absence of high-frequency details within radar imagery, complicating precise predictions by classifier models. Common strategies to mitigate this issue involve training on expansive datasets or employing more complex models, potentially susceptible to overfitting. However, generating sizeable datasets, especially for radar imagery, is challenging. Presenting an innovative solution, this study integrates a Convolutional Neural Network (CNN)-driven SR model with a classifier framework to enhance radar classification accuracy. The SR model is trained to upscale low-resolution millimetre-wave (mmW) images to high-resolution (HR) counterparts. These enhanced images serve as inputs for the classifier, distinguishing between threat and non-threat entities. Training data for the dual CNN layers is generated utilising a numerical model simulating a near-field coded-aperture computational imaging (CI) system. Evaluation of the resulting dual CNN model with simulated data yields a remarkable classification accuracy of 95%, accompanied by rapid inference time (0.193 seconds), rendering it suitable for real-time threat classification applications. Further validation with experimentally generated reconstruction data attests to the model’s robustness, achieving a classification accuracy of 94%. This integrated approach presents a promising solution for enhancing radar imagery analysis accuracy, offering substantial implications for real-world threat detection scenarios.