High-resolution and reliable automatic target recognition based on photonic ISAR imaging system with explainable deep learning

TL;DR

This paper presents a high-resolution ATR system using photonic ISAR imaging combined with explainable deep learning, enhancing recognition accuracy and reliability by linking physical imaging processes with CNN interpretability.

Contribution

It introduces a novel approach that integrates physical ISAR imaging characteristics with explainable CNN analysis, providing insights into how physical features influence deep learning recognition.

Findings

Broader bandwidths improve image detail and target distinction.

Physical imaging processes influence CNN feature extraction.

Explainability enhances trust and understanding of recognition results.

Abstract

Automatic target recognition (ATR) based on inverse synthetic aperture radar (ISAR) images, which is extensively utilized to surveil environment in military and civil fields, must be high-precision and reliable. Photonic technologies' advantage of broad bandwidth enables ISAR systems to realize high-resolution imaging, which is in favor of achieving high-performance ATR. Deep learning (DL) algorithms have achieved excellent recognition accuracies. However, the lack of interpretability of DL algorithms causes the head-scratching problem of credibility. In this paper, we exploit the inner relationship between a photonic ISAR imaging system and behaviors of a convolutional neural network (CNN) to deeply comprehend the intelligent recognition. Specifically, we manipulate imaging physical process and analyze network outputs, the relevance between the ISAR image and network output, and the visualization of features in the network output layer. Consequently, the broader imaging bandwidths and appropriate imaging angles lead to more detailed structural and contour features and the bigger discrepancy among ISAR images of different targets, which contributes to the CNN recognizing and distinguishing objects according to physical laws. Then, based on the photonic ISAR imaging system and the explainable CNN, we accomplish a high-accuracy and reliable ATR. To the best of our knowledge, there is no precedent of explaining the DL algorithms by exploring the influence of the physical process of data generation on network behaviors. It is anticipated that this work can not only inspire the accomplishment of a high-performance ATR but also bring new insights to explore network behaviors and thus achieve better intelligent abilities.