Xin Dai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xin Dai has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Lin Li
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Suihua Xue
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Xiaofei Yang
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Xiaohui Huang
School of Information and Software Engineering, East China Jiaotong University, Nanchang, China
1 shared publication - Zexi Li
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication
Latest Publications
- Open Access
- 9 July 2025
- Published by MDPI AG in Remote Sensing
- Vol. 17 (14) , 2361
HyperTransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Hyperspectral Image Classification
Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) insufficient synergy between spectral and spatial feature learning due to rigid coupling mechanisms; (2) high computational complexity resulting from redundant attention calculations; and (3) limited adaptability to spectral redundancy and noise in small-sample scenarios. To address these limitations, we propose HyperTransXNet, a novel CNN-Transformer hybrid architecture that incorporates adaptive spectral-spatial fusion. Specifically, the proposed HyperTransXNet comprises three key modules: (1) a Hybrid Spatial-Spectral Module (HSSM) that captures the refined local spectral-spatial features and models global spectral correlations by combining depth-wise dynamic convolution with frequency-domain attention; (2) a Mixture-of-Experts Routing (MoE-R) module that adaptively fuses multi-scale features by dynamically selecting optimal experts via Top-K sparse weights; and (3) a Spatial-Spectral Tokens Enhancer (SSTE) module that ensures causality-preserving interactions between spectral bands and spatial contexts. Extensive experiments on the Indian Pines, Houston 2013, and WHU-Hi-LongKou datasets demonstrate the superiority of HyperTransXNet.
- 9 July 2025
- Published by MDPI AG in Remote Sensing
- Vol. 17 (14) , 2361
Open AccessHyperTransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Hyperspectral Image Classification
Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) insufficient synergy between spectral and spatial feature learning due to rigid coupling mechanisms; (2) high computational complexity resulting from redundant attention calculations; and (3) limited adaptability to spectral redundancy and noise in small-sample scenarios. To address these limitations, we propose HyperTransXNet, a novel CNN-Transformer hybrid architecture that incorporates adaptive spectral-spatial fusion. Specifically, the proposed HyperTransXNet comprises three key modules: (1) a Hybrid Spatial-Spectral Module (HSSM) that captures the refined local spectral-spatial features and models global spectral correlations by combining depth-wise dynamic convolution with frequency-domain attention; (2) a Mixture-of-Experts Routing (MoE-R) module that adaptively fuses multi-scale features by dynamically selecting optimal experts via Top-K sparse weights; and (3) a Spatial-Spectral Tokens Enhancer (SSTE) module that ensures causality-preserving interactions between spectral bands and spatial contexts. Extensive experiments on the Indian Pines, Houston 2013, and WHU-Hi-LongKou datasets demonstrate the superiority of HyperTransXNet.