Progressive Frequency-Division Network for Hyperspectral and SAR Image Classification

Abstract

In light of the limitations of single-source remote sensing data for accurate Earth observation, integrating hyperspectral image (HSI) and synthetic aperture radar (SAR) data has been shown to be meaningful for land-cover classification task. Nevertheless, owing to the substantial disparities in imaging mechanisms and data attributes across different sources, existing classification methods still encounter certain challenges in adaptability and collaboration for frequency feature extraction and heterogeneous information fusion. As such, a new progressive frequency-division network is presented for joint classification of HSI and SAR data. Firstly, a progressive frequency decomposition module is designed to fully explore high- and low-frequency information of multisource remote sensing data, and minimize redundant information, thus achieving complementary and discriminative fusion feature extraction. After that, with an effective bidirectional propagation mechanism, a multiscale interaction module is further built to collaborate local and global information and explore more discriminative multiscale representations, thereby more accurately representing and classifying complex land covers. Experimental results for two public datasets containing HSI and SAR data reveal that the proposed model outperforms other competitors.

Keywords

multisource remote sensing data / land cover classification / progressive frequency decomposition / multiscale learning / bidirectional propagation mechanism