Hierarchical Attention and Parallel Filter Fusion Network for Multi-Source Data Classification
This work addresses a crucial challenge in remote sensing image interpretation, offering improved classification performance for multi-source data analysis.
The paper tackled the problem of joint classification of hyperspectral and synthetic aperture radar data in remote sensing by proposing a hierarchical attention and parallel filter fusion network, achieving overall accuracies of 91.44% and 80.51% on two datasets.
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 existing methods is deficient to exploit the abundant global, spectral, and local features simultaneously, leading to sub-optimal classification performance. To solve the problem, we propose a hierarchical attention and parallel filter fusion network for multi-source data classification. Concretely, we design a hierarchical attention module 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 which enhances cross-modal feature interactions among different spatial locations in the frequency domain. Extensive experiments on two multi-source 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.