Hierarchical Attention and Parallel Filter Fusion Network for Multisource Data Classification

Hyperspectral image (HSI) and synthetic aperture radar (SAR) data joint classification is a crucial and yet challenging task in the field of remote sensing image interpretation. However, feature modeling in the existing methods is deficient to exploit the abundant global, spectral, and local features simultaneously, leading to suboptimal classification performance. To solve the problem, we propose a hierarchical attention and parallel filter fusion network for multisource data classification. Concretely, we design a hierarchical attention module (HAM) for hyperspectral feature extraction. This module integrates global, spectral, and local features simultaneously to provide more comprehensive feature representation. In addition, we develop parallel filter fusion module (PFFM), which enhances cross-modal feature interactions among different spatial locations in the frequency domain. Extensive experiments on two multisource remote sensing data classification datasets verify the superiority of our proposed method over current state-of-the-art classification approaches. Specifically, our proposed method achieves 91.44% and 80.51% of overall accuracy (OA) on the respective datasets, highlighting its superior performance.