Yingxia Chen at Yangtze University, China.
Prof. Yingxia Chen is a leading Chinese scholar in software engineering, specializing in machine learning and image processing. As a professor and department director, he combines academic leadership with high-impact research and mentorship of doctoral students. A prolific contributor to national scientific initiatives, he has led over 20 research projects and published extensively in top-tier SCI journals. His affiliations with IEEE and major Chinese scientific societies underscore his prominence in both national and international research communities.
Professional Profile
Orcid
🎓 Education
Dr. Yingxia Chen earned his doctoral degree in a field aligned with computer science and engineering, with a specialization in machine learning and image processing. His academic journey laid a strong foundation for his subsequent contributions to advanced research and leadership in software engineering.
💼 Experience
Prof. Yingxia Chen is a distinguished academic and researcher, currently serving as a Professor, Doctoral Supervisor, and Director of the Department of Software Engineering. He is a member of IEEE, China Artificial Intelligence Society, and the China Image and Graphics Society. Additionally, he is recognized as an expert in the Hubei Science and Technology Expert Database and the Jingzhou Science and Technology Expert Database.
As a leading contributor to national research, Prof. Chen has participated in four major national projects, including the 973 Project, National Natural Science Foundation Key Project, and Major Projects. He has independently led over 20 research projects and actively contributed to China’s scientific and technological innovation landscape.
Prof. Chen has also served as a reviewer for several SCI-indexed journals, maintaining a strong presence in academic peer review and quality control in scientific publishing.
🔬 Research Interests
📊 Author Metrics
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Total Publications: 40+
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SCI-Indexed Papers: 30+
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Research Projects Led: 20+
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National Key Projects Participated: 4
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Journal Reviewer Roles: Multiple SCI-indexed journals
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Professional Affiliations: IEEE, CAIS, CIGS, Hubei and Jingzhou Tech Expert Databases
Top Noted Publications:
1. AMS: A Hyperspectral Image Classification Method based on SVM and Multi-modal Attention Network
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Journal: Knowledge-Based Systems
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Publication Date: February 2025
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DOI: 10.1016/j.knosys.2025.113236
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ISSN: 0950-7051
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Contributors: Yingxia Chen, Zhaoheng Liu, Zeqiang Chen
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Summary:
This paper introduces AMS, a novel classification method for hyperspectral images that integrates Support Vector Machines (SVM) with a multi-modal attention mechanism. The approach aims to enhance the discriminative ability of spectral-spatial features in hyperspectral image (HSI) classification tasks.
2. CSLP: A Novel Pansharpening Method Based on Compressed Sensing and L-PNN
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Journal: Information Fusion
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Publication Date: February 6, 2025
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DOI: 10.1016/j.inffus.2025.103002
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ISSN: 1566-2535
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Contributors: Yingxia Chen, Zhenglong Wan, Zeqiang Chen, Mingming Wei
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Summary:
The paper proposes CSLP, a new pansharpening method that utilizes Compressed Sensing (CS) techniques and Lagrangian Projected Neural Networks (L-PNN) to fuse high-resolution panchromatic and low-resolution multispectral images. The model enhances spatial and spectral fidelity for remote sensing applications.
3. A Method Based on Hybrid Cross-Multiscale Spectral-Spatial Transformer Network for Hyperspectral and Multispectral Image Fusion
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Journal: Expert Systems with Applications
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Publication Date: November 10, 2024
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DOI: 10.1016/j.eswa.2024.125742
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ISSN: 0957-4174
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Contributors: Yingxia Chen, Mingming Wei, Yan Chen
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Summary:
This research develops a hybrid cross-multiscale transformer network for fusing hyperspectral and multispectral data, improving both spatial detail retention and spectral accuracy. It leverages attention-based transformer modules to capture rich multi-level dependencies across modalities.
4. MMCMOO: A Novel Multispectral Pansharpening Method
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Journal: Mathematics
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Publication Date: July 2024
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DOI: 10.3390/math12142255
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Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
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Contributors: Yingxia Chen, Yingying Xu
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Summary:
The MMCMOO method (Multi-scale Multichannel Mixed Optimization Objective) introduces an advanced pansharpening technique using multiscale optimization and mixed spectral-spatial objective functions. This method aims to preserve image fidelity across complex multispectral datasets.
5. DPDU-Net: Double Prior Deep Unrolling Network for Pansharpening
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Journal: Remote Sensing
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Publication Date: June 2024
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DOI: 10.3390/rs16122141
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Publisher: MDPI
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Contributors: Yingxia Chen, Yuqi Li, Tingting Wang, Yan Chen, Faming Fang
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Summary:
DPDU-Net is a deep learning-based pansharpening network that incorporates double prior knowledge—spectral and spatial—to guide the deep unrolling optimization process. It achieves superior performance in resolution enhancement while minimizing distortions in remote sensing images.
Conclusion:
Prof. Yingxia Chen is a top-tier candidate for the Best Researcher Award in Image Processing. His innovative contributions, leadership roles, and deep commitment to national and academic advancement reflect the qualities of a transformative researcher. With his proven record of excellence, Prof. Chen stands out as a role model in computational imaging and AI-based visual intelligence.