A Feature Fusion Approach to Classifying Targets Underneath Foliage via Wideband LFMB SAR Systems

We investigate the benefits of employing a wideband low-frequency multiple-band (LFMB) system for classifying targets underneath foliage. While previous work in this area, for both single and multiband scenarios, has been limited to lower bandwidth (<; = 500 MHz) regimes, in this letter, we explore the complementary benefits of incorporating multiple radar bands to improve the overall classification accuracy when employing a wideband (>500 MHz) LFMB synthetic aperture radar (SAR) system. After describing our novel LFMB SAR system in detail and investigating foliage penetration (FOPEN) phenomenologies in target detection applications, we then focus on the main topic of this letter, viz., classification of targets underneath foliage. In particular, we demonstrate feature-classifier combinations that successfully leverage multiband information and that deliver superior performance to each individual band alone. All our experimental validations are based on data captured by our wideband LFMB SAR system.