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学者姓名：翁谦
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Abstract ：
波段选择是高光谱遥感图像降维的一项重要任务,其目标是选择包含较少冗余信息、较大信息量和具有类别可分性的波段子集.为解决基于近邻子空间划分的波段选择方法没有考虑地物空间分布和计算聚类中心时忽略噪声波段影响的问题,本文提出了一种结合空谱结构与改进局部密度的高光谱图像波段选择方法.该方法首先对高光谱图像进行基于熵率的图像分割获得高光谱图像同质区域,综合同质区域相关系数矩阵获得图像区域级近邻波段相关系数向量;其次,用高斯核平滑全局近邻波段相关系数向量以降低噪声波段的影响,并根据极值点进行波段分组;然后,最大化改进局部密度和波段信息熵的乘积作为选取代表性波段的标准;最后,在Indian Pines、Botswana和Salinas高光谱图像数据集上,通过SVM、KNN和LDA分类器上进行分类实验.结果表明:(1)对比像素级相关系数划分方法,利用区域级相关系数使得近邻波段分组更为合理,降低波段冗余性,同时还保留了部分潜在特征波段,在3个数据集上的分类性能分别提高了 2.63％,0.68％,0.16％;(2)对比仅使用信息熵的波段衡量方法,本文提出的最大化改进的局部密度和信息熵乘积的方法是有效的,在3个数据集上OA分别提高了 4.13％、0.5％和0.21％;(3)对比其他6种先进波段选择方法,本文方法在3个数据集上的OA分别从62.34％提高到75.03％、86.74％提高到88.28％和86.04％提高到92.36％.此外,所选择的波段子集在分布上较为分散,主要集中在信息熵较高的区域,同时避免了选择噪声波段.综上,本文提出的波段选择方法所得波段子集具有较低的冗余性、丰富的信息量、强的类别可分性,并且对噪声具有较强的鲁棒性,能够有效地解决高光谱图像波段选择的问题.
Keyword ：
信息熵 信息熵 分类 分类 子空间划分 子空间划分 局部密度 局部密度 峰值密度 峰值密度 波段选择 波段选择 遥感 遥感 高光谱图像 高光谱图像
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| GB/T 7714 | 翁谦 , 安远 , 陈光剑 et al. 结合空谱结构与改进局部密度的高光谱图像波段选择 [J]. | 遥感学报 , 2025 , 29 (1) : 247-265 . |
| MLA | 翁谦 et al. "结合空谱结构与改进局部密度的高光谱图像波段选择" . | 遥感学报 29 . 1 (2025) : 247-265 . |
| APA | 翁谦 , 安远 , 陈光剑 , 吴瑞姣 , 林嘉雯 . 结合空谱结构与改进局部密度的高光谱图像波段选择 . | 遥感学报 , 2025 , 29 (1) , 247-265 . |
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在高分遥感图像语义分割中,为解决如何有效融合光谱信息与高程信息以分割相似光谱的不同地物的问题和通过捕获长距离依赖信息来提升局部地物识别精度,本文提出一种面向多源数据的多区域尺度协同语义分割方法.该方法包括:一种不等长的多分支语义分割网络,以有效提取多源特征,充分利用多源数据之间的互补信息;一个轻量级的协同注意力特征融合模块,用于在特征融合阶段有效地融合多分支特征;一种多区域尺度协同的数据增强方法,引导网络捕获长距离依赖信息.在ISPRS提供的公开数据集Vaihingen和Potsdam上的实验结果表明,与同类型主流方法对比,本文提出的方法具有更优的分割性能,且得到的地物细节信息更加完整,参数量也更小.
Keyword ：
协同注意力 协同注意力 多源数据融合 多源数据融合 数字表面模型 数字表面模型 语义分割 语义分割 高分遥感图像 高分遥感图像
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| GB/T 7714 | 林易丰 , 陈光剑 , 陈浩 et al. 面向多源数据的多区域尺度协同高分遥感图像语义分割 [J]. | 小型微型计算机系统 , 2025 , 46 (1) : 158-166 . |
| MLA | 林易丰 et al. "面向多源数据的多区域尺度协同高分遥感图像语义分割" . | 小型微型计算机系统 46 . 1 (2025) : 158-166 . |
| APA | 林易丰 , 陈光剑 , 陈浩 , 翁谦 , 林嘉雯 . 面向多源数据的多区域尺度协同高分遥感图像语义分割 . | 小型微型计算机系统 , 2025 , 46 (1) , 158-166 . |
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In the semantic segmentation of high-resolution remote sensing images, utilizing the normalized Digital Surface Model (nDSM) that provides height information as auxiliary data and fusing it with the visible image can improve the accuracy of segmentation. However, the better utilization of complementarity between different modal features has not been fully explored. In this work, we propose a new dual-branch and multi-stage Bimodal Fusion Rectification Network (BFRNet), which is end-to-end trainable. It consists of three modules: Channel and Spatial Fusion Rectification (CSFR) module, Edge Fusion Refinement (EFR) module, and Multiscale Feature Fusion (MSFF) module. The CSFR module integrates and rectifies multimodal features in both channel and spatial dimensions, achieving sufficient interaction and fusion between multimodal features. The EFR module obtains better multiscale edge features than single modality through feature fusion based on bimodal interactive edge attention and spatial gate, which helps to alleviate the edge loss of ground objects in single modality. The MSFF module is used to upsample and fuse multiscale features from EFR and CSFR to generate the final semantic segmentation results. The experimental results on the two public datasets, Vaihingen and Potsdam, provided by ISPRS, showcase the comparative advantage of the proposed method over other research methods.
Keyword ：
Feature Rectification Feature Rectification Modal Fusion Modal Fusion Remote Sensing Remote Sensing Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Weng, Qian , Lin, Yifeng , Pan, Zengying et al. BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PT XIII, PRCV 2024 , 2025 , 15043 : 501-515 . |
| MLA | Weng, Qian et al. "BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation" . | PATTERN RECOGNITION AND COMPUTER VISION, PT XIII, PRCV 2024 15043 (2025) : 501-515 . |
| APA | Weng, Qian , Lin, Yifeng , Pan, Zengying , Lin, Jiawen , Chen, Gengwei , Chen, Mo et al. BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation . | PATTERN RECOGNITION AND COMPUTER VISION, PT XIII, PRCV 2024 , 2025 , 15043 , 501-515 . |
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In recent years, sound source localization methods based on Direct Path Interchannel Phase Difference (DP-IPD) estimation have gained considerable attention, owing to their outstanding performance in noisy environments. However, existing methods face several challenges when processing data from multi-element arrays, as the network must learn the complex mapping relationships between signals and features across multiple microphone pairs. These mapping relationships share a certain degree of similarity, which makes it challenging for the network to differentiate them, ultimately impacting localization accuracy. Furthermore, the time-varying nature of spatial cues caused by a moving sound source can further degrade the performance of localization methods. To tackle these challenges, this paper introduces a Time-Frequency Feature Enhanced Convolutional Recurrent Neural Network. By incorporating a frequency attention convolution module and a gated convolution module, the proposed network adaptively handles the mapping of signals to features across different microphone pairs while enhancing its ability to extract local temporal context. This approach improves localization accuracy for moving sound sources in noisy environments. Extensive experimental results show that the proposed method significantly outperforms state-of-the-art approaches on both simulated and real-world datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Acoustic generators Acoustic generators Acoustic noise measurement Acoustic noise measurement Convolution Convolution Convolutional neural networks Convolutional neural networks Data handling Data handling Mapping Mapping Microphones Microphones Signal processing Signal processing
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| GB/T 7714 | Chen, Mo , Weng, Qian , Zeng, Chenjie et al. Time-Frequency Feature Enhancement Method for Moving Multiple Sound Source Localization in Noisy Environments [C] . 2025 : 376-387 . |
| MLA | Chen, Mo et al. "Time-Frequency Feature Enhancement Method for Moving Multiple Sound Source Localization in Noisy Environments" . (2025) : 376-387 . |
| APA | Chen, Mo , Weng, Qian , Zeng, Chenjie , Lin, Jiawen , Kang, Yuanxun . Time-Frequency Feature Enhancement Method for Moving Multiple Sound Source Localization in Noisy Environments . (2025) : 376-387 . |
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Band selection is a crucial task in the dimensionality reduction of hyperspectral remote sensing imagery. Its objective is to choose a subset of bands with minimal redundant information, high information content, and class discriminability. To address the issues of existing band selection methods based on nearest neighbor subspace partitioning, which do not consider the spatial distribution of objects and neglect the effect of noisy bands when computing cluster centers, this study proposes a hyperspectral image band selection method that integrates a spatial-spectral structure with improved local density, referred to as ISSS-ELD. This method first performs image segmentation on the hyperspectral image using an entropy-based approach to acquire homogeneous regions. A composite region-level neighboring band correlation coefficient vector is obtained by integrating the correlation coefficient matrix of these homogeneous regions. Subsequently, a Gaussian kernel is applied to globally smooth the neighboring band correlation coefficient vector, reducing the influence of noisy bands. Bands are grouped on the basis of extremum points in the smoothed vector. The product of the maximized improved local density and band information entropy serves as the criterion for selecting representative bands. This study conducts experiments on hyperspectral image datasets, including Indian Pines, Botswana, and Salinas. Different band selection methods are evaluated by calculating metrics such as classification accuracy, average correlation coefficient, and noise robustness of the selected bands. The results are as follows: (1) Compared with pixel-level correlation-based partitioning methods, the utilization of region-level correlation coefficients results in more reasonable grouping of neighboring bands, reducing band redundancy while retaining some potential characteristic bands. The classification performance on the three datasets is improved by 2.63%, 0.68%, and 0.16%. (2) In contrast with methods solely using information entropy for band assessment, the proposed approach of maximizing the product of improved local density and information entropy proves effective. On the three datasets, the Overall Accuracy (OA) is increased by 4.13%, 0.5%, and 0.21%. (3) Compared with six other advanced band selection methods, the proposed method achieves significant performance improvements: OA is increased from 62.34% to 75.03%, from 86.74% to 88.28%, and from 86.04% to 92.36% on the three datasets. Furthermore, the selected subset of bands by our method is dispersed, concentrating in regions with higher information entropy and effectively avoiding the inclusion of noisy bands. In summary, the band subset selected by the proposed band selection method exhibits low redundancy, high information content, strong class separability, and robustness against noise, effectively addressing the challenges in hyperspectral image band selection. © 2025 Science Press. All rights reserved.
Keyword ：
Image correlation Image correlation Image enhancement Image enhancement Image segmentation Image segmentation Network coding Network coding Redundancy Redundancy
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| GB/T 7714 | Weng, Qian , An, Yuan , Chen, Guangjian et al. Integration of a spatial-spectral structure with improved local density for hyperspectral image band selection [J]. | National Remote Sensing Bulletin , 2025 , 29 (1) : 246-264 . |
| MLA | Weng, Qian et al. "Integration of a spatial-spectral structure with improved local density for hyperspectral image band selection" . | National Remote Sensing Bulletin 29 . 1 (2025) : 246-264 . |
| APA | Weng, Qian , An, Yuan , Chen, Guangjian , Wu, Ruijiao , Lin, Jiawen . Integration of a spatial-spectral structure with improved local density for hyperspectral image band selection . | National Remote Sensing Bulletin , 2025 , 29 (1) , 246-264 . |
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The rapid advancement of Industry 4.0 has revolutionized manufacturing, shifting production from centralized control to decentralized, intelligent systems. Smart factories are now expected to achieve high adaptability and resource efficiency, particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands. To address the challenges of dynamic task allocation, uncertainty, and realtime decision-making, this paper proposes Pathfinder, a deep reinforcement learning-based scheduling framework. Pathfinder models scheduling data through three key matrices: execution time (the time required for a job to complete), completion time (the actual time at which a job is finished), and efficiency (the performance of executing a single job). By leveraging neural networks, Pathfinder extracts essential features from these matrices, enabling intelligent decision-making in dynamic production environments. Unlike traditional approaches with fixed scheduling rules, Pathfinder dynamically selects from ten diverse scheduling rules, optimizing decisions based on real-time environmental conditions. To further enhance scheduling efficiency, a specialized reward function is designed to support dynamic task allocation and real-time adjustments. This function helps Pathfinder continuously refine its scheduling strategy, improving machine utilization and minimizing job completion times. Through reinforcement learning, Pathfinder adapts to evolving production demands, ensuring robust performance in real-world applications. Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches, offering improved coordination and efficiency in smart factories. By integrating deep reinforcement learning, adaptable scheduling strategies, and an innovative reward function, Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
Keyword ：
customization customization deep reinforcement learning deep reinforcement learning multi-robot system multi-robot system production scheduling production scheduling Smart factory Smart factory task allocation task allocation
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| GB/T 7714 | Lyu, Chenxi , Dong, Chen , Xiong, Qiancheng et al. Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization [J]. | CMC-COMPUTERS MATERIALS & CONTINUA , 2025 , 84 (2) : 3371-3391 . |
| MLA | Lyu, Chenxi et al. "Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization" . | CMC-COMPUTERS MATERIALS & CONTINUA 84 . 2 (2025) : 3371-3391 . |
| APA | Lyu, Chenxi , Dong, Chen , Xiong, Qiancheng , Chen, Yuzhong , Weng, Qian , Chen, Zhenyi . Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization . | CMC-COMPUTERS MATERIALS & CONTINUA , 2025 , 84 (2) , 3371-3391 . |
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ZY-1 02E satellite is a Chinese hyperspectral satellite launched in recent years. There are few reports on its application at present, so there is no suitable research method. In this study, rice planting area was extracted from two experimental areas of Fujian Province using Ziyuan No. 1 02E hyperspectral data. A multi-scale spectral feature extraction network Based on spatial neighborhood (MSFEN-BSN) was proposed to solve the high spectral resolution, low spatial resolution and complex and fragmented distribution of crops in the experimental area. The network consists of two modules: Multi-scale Spectral Feature Extraction (MSFE) and Spatial Neighborhood Feature Fusion (SNFF), which can extract spectral features at multiple scales to obtain deep spectral semantics, and fuse spectral-spatial information of neighboring pixels to obtain more effective classification features. The mapping accuracy of rice classification in two experimental areas reached 83.61% and 84.36% respectively, which can meet the needs of practical application. Meanwhile, to prove the robustness of the proposed method, precision verification is carried out on two public vegetation datasets Indian pines (IP) and Salinas Valley (SA). Compared with some traditional vegetation extraction methods and classical deep learning methods, the proposed method has higher precision. © 2024 Copyright held by the owner/author(s).
Keyword ：
Convolutional neural networks Convolutional neural networks Deep learning Deep learning Vegetation mapping Vegetation mapping
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| GB/T 7714 | Pan, Zengying , Wu, Ruijiao , Chen, Gengwei et al. Extraction of Rice Planting Range Based on Hyperspectral Data of ZY-1 02E [C] . 2024 : 321-327 . |
| MLA | Pan, Zengying et al. "Extraction of Rice Planting Range Based on Hyperspectral Data of ZY-1 02E" . (2024) : 321-327 . |
| APA | Pan, Zengying , Wu, Ruijiao , Chen, Gengwei , Jian, Cairen , Weng, Qian . Extraction of Rice Planting Range Based on Hyperspectral Data of ZY-1 02E . (2024) : 321-327 . |
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Accurate building extraction for high-resolution remote sensing images is critical for topographic mapping, urban planning, and many other applications. Its main task is to label each pixel point as a building or non-building. Although deep-learning-based algorithms have significantly enhanced the accuracy of building extraction, fully automated methods for building extraction are limited by the requirement for a large number of annotated samples, resulting in a limited generalization ability, easy misclassification in complex remote sensing images, and higher costs due to the need for a large number of annotated samples. To address these challenges, this paper proposes an improved interactive building extraction model, ARE-Net, which adopts a deep interactive segmentation approach. In this paper, we present several key contributions. Firstly, an adaptive-radius encoding (ARE) module was designed to optimize the interaction features of clicks based on the varying shapes and distributions of buildings to provide maximum a priori information for building extraction. Secondly, a two-stage training strategy was proposed to enhance the convergence speed and efficiency of the segmentation process. Finally, some comprehensive experiments using two models of different sizes (HRNet18s+OCR and HRNet32+OCR) were conducted on the Inria and WHU building datasets. The results showed significant improvements over the current state-of-the-art method in terms of NoC90. The proposed method achieved performance enhancements of 7.98% and 13.03% with HRNet18s+OCR and 7.34% and 15.49% with HRNet32+OCR on the WHU and Inria datasets, respectively. Furthermore, the experiments demonstrated that the proposed ARE-Net method significantly reduced the annotation costs while improving the convergence speed and generalization performance.
Keyword ：
adaptive-radius encoding adaptive-radius encoding interactive building extraction interactive building extraction remote sensing remote sensing two-stage training two-stage training
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| GB/T 7714 | Weng, Qian , Wang, Qin , Lin, Yifeng et al. ARE-Net: An Improved Interactive Model for Accurate Building Extraction in High-Resolution Remote Sensing Imagery [J]. | REMOTE SENSING , 2023 , 15 (18) . |
| MLA | Weng, Qian et al. "ARE-Net: An Improved Interactive Model for Accurate Building Extraction in High-Resolution Remote Sensing Imagery" . | REMOTE SENSING 15 . 18 (2023) . |
| APA | Weng, Qian , Wang, Qin , Lin, Yifeng , Lin, Jiawen . ARE-Net: An Improved Interactive Model for Accurate Building Extraction in High-Resolution Remote Sensing Imagery . | REMOTE SENSING , 2023 , 15 (18) . |
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提出一种多层次自适应知识蒸馏方法,以提升轻量化模型的性能.首先,针对遥感影像类别间差异程度不均衡的问题,通过改进输出层知识蒸馏中的温度机制,提出一种自适应温度机制,促进学生模型更好地学习大且深的教师模型输出层概率分布知识;然后,通过添加辅助卷积块来融入特征层的知识蒸馏方法,使学生模型学习教师模型的多层次知识;最后,在UCM、AID和NWPU这 3 个公开数据集上进行实验.结果表明:所提方法蒸馏后的学生模型参数量仅为教师模型的 6%,其分类精度较蒸馏前最多可提升 7.78%,比其他网络模型更便于部署在末端.
Keyword ：
卷积神经网络 卷积神经网络 场景分类 场景分类 特征蒸馏 特征蒸馏 知识蒸馏 知识蒸馏 自适应温度蒸馏 自适应温度蒸馏
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| GB/T 7714 | 翁谦 , 黄志铭 , 林嘉雯 et al. 多层次自适应知识蒸馏的轻量化高分遥感场景分类 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (4) : 459-466 . |
| MLA | 翁谦 et al. "多层次自适应知识蒸馏的轻量化高分遥感场景分类" . | 福州大学学报（自然科学版） 51 . 4 (2023) : 459-466 . |
| APA | 翁谦 , 黄志铭 , 林嘉雯 , 简彩仁 , 廖祥文 . 多层次自适应知识蒸馏的轻量化高分遥感场景分类 . | 福州大学学报（自然科学版） , 2023 , 51 (4) , 459-466 . |
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Models based on convolutional neural networks (CNNs) have achieved remarkable advances in high-resolution remote sensing (HRRS) images scene classification, but there are still challenges due to the high similarity among different categories and loss of local information. To address this issue, a multigranularity alternating feature mining (MGA-FM) framework is proposed in this article to learn and fuse both global and local information for HRRS scene classification. First, a region confusion mechanism is adopted to guide network's shallow layers to adaptively learn the salient features of distinguishing regions. Second, an alternating comprehensive training strategy is designed to capture and fuse shallow local feature information and deep semantic information to enhance feature representation capabilities. In particular, the MGA-FM framework can be flexibly embedded in various CNN backbone networks as a training mechanism. Extensive experimental results and visualization analysis on three remote sensing scene datasets indicated that the proposed method can achieve competitive classification performance.
Keyword ：
Convolutional neural network (CNN) Convolutional neural network (CNN) feature mining feature mining local detailed information local detailed information remote sensing image remote sensing image scene classification scene classification
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| GB/T 7714 | Weng, Qian , Huang, Zhiming , Lin, Jiawen et al. Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 : 318-330 . |
| MLA | Weng, Qian et al. "Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16 (2023) : 318-330 . |
| APA | Weng, Qian , Huang, Zhiming , Lin, Jiawen , Jian, Cairen , Mao, Zhengyuan . Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 , 318-330 . |
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