| 标题 |
 Bilinear Parallel Fourier Transformer for Multimodal Remote Sensing Classification 用于多模态遥感分类的双线性并行傅里叶变换
相关领域
计算机科学
双线性插值
遥感
傅里叶变换
模式识别（心理学）
人工智能
计算机视觉
地质学
数学
数学分析
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| 网址 | |
| DOI | |
| 其它 | Vision Transformers (ViTs) have shown promise in multimodal fusion image classification, yet face performance challenges in complex remote sensing scenarios. Single fusion frameworks often fail to fully utilize multimodal diversity, and the uneven distribution of image categories complicates the accurate construction of spatial structures by Transformers. Additionally, traditional cross-entropy tends to favor majority classes, neglecting minority classes, resulting in suboptimal predictions and reduced overall accuracy (OA). To solve these challenges, we propose a novel deep neural network, a bilinear parallel Fourier Transformer (BPFT). We propose a novel dual-fusion feature interaction (DFFI) module that utilizes two distinct types of fused features for learning, namely the spatial-spectral fusion feature and the global fusion feature. Besides, we introduce a dual-feature interaction (DFI) module to improve the utilization of fused feature information. |
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