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学者姓名：卢孝强
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在我国统筹实施科教兴国战略、人才强国战略、创新驱动发展战略,以及一体推进教育发展、科技创新、人才培养政策的引领与驱动下,高校、企业与科研院所之间的协同育人机制已经从最初的倡议和试点阶段,逐渐迈向了落地实施和深入发展时期.科教融合与产教融合协同育人模式将得到进一步的深化,主要体现在培养主体的多元化、培养层次的提升、培养机制的优化以及培养方式的创新与升级等多个方面.以福州大学物理与信息工程学院为例,针对电子信息领域国家技术和人才战略需求,探索重点高校、头部企业和科研机构通过设立定制化专班、共建科研平台、面向头部企业定向就业、共同举办学术交流论坛、共建导师团队、共同评价培养质量等方式实施研究生培养,打造全要素融合研究生培养新范式,着力实现创新型高层次人才自主培养.
Keyword ：
产教融合 产教融合 校企联合专班 校企联合专班 研究生培养 研究生培养 科教融合 科教融合
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| GB/T 7714 | 杨晓丹 , 柯颖莹 , 郑志刚 et al. 全要素融合的研究生产学研协同培养机制构建研究 [J]. | 中国高校科技 , 2025 , (2) : 93-96 . |
| MLA | 杨晓丹 et al. "全要素融合的研究生产学研协同培养机制构建研究" . | 中国高校科技 2 (2025) : 93-96 . |
| APA | 杨晓丹 , 柯颖莹 , 郑志刚 , 魏金明 , 卢孝强 , 李福山 . 全要素融合的研究生产学研协同培养机制构建研究 . | 中国高校科技 , 2025 , (2) , 93-96 . |
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In remote sensing of Earth observation, multi-source data can be captured by multiple platforms, multiple sensors, and multiple perspectives. These data provide complementary information for interpreting remote sensing scenes. Although these data offer richer information, they also increase the demand for model depth and complexity. Deep learning plays a pivotal role in unlocking the potential of remote sensing data by delving deep into the semantic layers of scenes and extracting intricate features from images. Recent advancements in artificial intelligence have greatly enhanced this process. However, deep learning networks have limitations when applied to remote sensing images. 1)The huge number of parameters and the difficulty in training, as well as the over-reliance on labeled training data, can affect these images. Remote sensing images are characterized by“data miscellaneous marking difficulty”, which makes manual labeling insufficient for meeting the training needs of deep learning. 2)Variations in remote sensing platforms, sensors, shooting angles, resolution, time, location, and weather can all impact remote sensing images. Thus, the interpreted images and training samples cannot have the same distribution. This inconsistency results in weak generalization ability in existing models, especially when dealing with data from different distributions. To address this issue, cross-domain remote sensing scene interpretation aims to train a model on labeled remote sensing scene data(source domain)and apply it to new, unlabeled scene data(target domain)in an appropriate way. This approach reduces the dependence on target domain data and relaxes the assumption of the same distribution in existing deep learning tasks. The shallow layers of convolutional neural networks can be used as general-purpose feature extractors, but deeper layers are more task-specific and may introduce bias when applied to other tasks. Therefore, the migration model must be modified to accomplish the task of interpreting the target domain. Cross-domain interpretation tasks aim to establish a model that can adapt to various scene changes by utilizing migration learning, domain adaptation and other techniques for reducing model prediction inaccuracy caused by changes in the data domain. This approach improves the robustness and generalization ability of the model. Interpreting cross-domain remote sensing scenes typically requires using data from multiple remote sensing sources, including radar, aerial and satellite imagery. These images may have varying views, resolutions, wavelength bands, lighting conditions and noise levels. They may also originate from different locations or sensors. As the Global Earth Observation Systems continues to advance, remote sensing images now include cross-platform, cross-sensor, cross-resolution, and cross-region, which results in enormous distributional variances. Therefore, the study of cross-domain remote sensing scene interpretation is essential for the commercial use of remote sensing data and has theoretical and practical importance. This report categorizes scene decoding tasks into four main types based on the labeled set of data：methods based on closed-set domain adaptation, partial-domain adaptation, open-set domain adaptation and generalized domain adaptation. Approaches based on closed-set domain adaptation focus on tasks where the label set of the target domain is the same as that of the source domain. Partial domain adaptation focuses on tasks where the label set of the target domain is a subset of the source domain. Open-set domain adaptation aims to research tasks where the label set of the source domain is a subset of the label set of the target domain, and it does not apply restrictions in the approach of generalized domain adaptation. This study provides an in-depth investigation of two typical tasks in cross-domain remote sensing interpretation：scene recognition and target knowledge. The first part of the study utilizes domestic and international literature to provide a comprehensive assessment of the current research status of the four types of methods. Within the target recognition task, cross-domain tasks are further subdivided into cross-domain for visible light data and cross-domain from visible light to Synthetic Aperture Radar images. After a quantitative analysis of the sample distribution characteristics of different datasets, a unified experimental setup for cross-domain tasks is proposed. In the scene classification task, the dataset is explored by classifying it according to the label set categorization, and specific examples are given to provide the corresponding experimental setup for the readers’reference. The fourth part of the study discusses the research trends in cross-domain remote sensing interpretation, which highlights four challenging research directions：few-shot learning, source domain data selection, multi-source domain interpretation, and cross-modal interpretation. These areas will be important directions for the future development of remote sensing scene interpretation, which offers potential choices for readers’subsequent research directions. © 2024 Editorial and Publishing Board of JIG. All rights reserved.
Keyword ：
adaptive algorithm adaptive algorithm cross-domain remote sensing scene interpretation cross-domain remote sensing scene interpretation diverse dataset diverse dataset migration learning migration learning model generalization model generalization out-of-distribution generalization out-of-distribution generalization
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| GB/T 7714 | Zheng, X. , Xiao, X. , Chen, X. et al. Advancements in cross-domain remote sensing scene interpretation; [跨域遥感场景解译研究进展] [J]. | Journal of Image and Graphics , 2024 , 29 (6) : 1730-1746 . |
| MLA | Zheng, X. et al. "Advancements in cross-domain remote sensing scene interpretation; [跨域遥感场景解译研究进展]" . | Journal of Image and Graphics 29 . 6 (2024) : 1730-1746 . |
| APA | Zheng, X. , Xiao, X. , Chen, X. , Lu, W. , Liu, X. , Lu, X. . Advancements in cross-domain remote sensing scene interpretation; [跨域遥感场景解译研究进展] . | Journal of Image and Graphics , 2024 , 29 (6) , 1730-1746 . |
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Modern detectors are mostly trained under single and limited conditions. However, object detection faces various complex and open situations in autonomous driving, especially in urban street scenes with dense objects and complex backgrounds. Due to the shift in data distribution, modern detectors cannot perform well in actual urban environments. Using domain adaptation to improve detection performance is one of the key methods to extend object detection from limited situations to open situations. To this end, this article proposes a Domain Adaptation of Anchor -Free object detection (DAAF) for urban traffic. DAAF is a crossdomain object detection method that performs feature alignment including two aspects. On the one hand, we designed a fully convolutional adversarial training method for global feature alignment at the image level. Meanwhile, images can generally be decomposed into structural information and texture information. In urban street scenes, the structural information of images is generally similar. The main difference between the source domain and the target domain is texture information. Therefore, during global feature alignment, this paper proposes a method called texture information limitation (TIL). On the other hand, in order to solve the problem of variable aspect ratios of objects in urban street scenes, this article uses an anchor -free detector as the baseline detector. Since the anchor -free object detector can obtain neither explicit nor implicit instance -level features, we adopt Pixel -Level Adaptation (PLA) to align local features instead of instance -level alignment for local features. The size of the object has the greatest impact on the final detection effect, and the object scale in urban scenes is relatively rich. Guided by the differentiation of attention mechanisms, a multi -level adversarial network is designed to perform feature alignment of the output space at different feature levels called Scale Information Limitation (SIL). We conducted cross -domain detection experiments by using various urban streetscape autonomous driving object detection datasets, including adverse weather conditions, synthetic data to real data, and cross -camera adaptation. The experimental results indicate that the method proposed in this article is effective.
Keyword ：
Domain adaptation Domain adaptation Object detection Object detection Urban traffic Urban traffic
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| GB/T 7714 | Yu, Xiaoyong , Lu, Xiaoqiang . Domain Adaptation of Anchor-Free object detection for urban traffic [J]. | NEUROCOMPUTING , 2024 , 582 . |
| MLA | Yu, Xiaoyong et al. "Domain Adaptation of Anchor-Free object detection for urban traffic" . | NEUROCOMPUTING 582 (2024) . |
| APA | Yu, Xiaoyong , Lu, Xiaoqiang . Domain Adaptation of Anchor-Free object detection for urban traffic . | NEUROCOMPUTING , 2024 , 582 . |
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In recent years, with the continuous advancement of remote sensing (RS) technology and text processing techniques, there has been a growing abundance of RS images and associated textual data. Combining RS images with their corresponding textual data allows for integrated analysis and retrieval, which holds significant practical implications across multiple application domains, including geographic information systems (GIS), environmental monitoring, and agricultural management. RS images have the characteristics of multitargets and multiscales, and the textual descriptions of these targets are not fully utilized, leading to a decrease in retrieval accuracy. Previous methods have struggled to balance intermodality information interaction and intramodality feature fusion, and they have paid little attention to the consistency of distribution within modalities. In light of this, this article proposes a symmetric multilevel guidance network (SMLGN) for cross-modal retrieval in RS. SMLGN first introduces fusion guidance between local and global within modalities and fine-grained bidirectional guidance between modalities, allowing for the learning of a common semantic space. Furthermore, to address the distribution differences of different modalities within the common semantic space, we design an adversarial joint learning framework and a multiobjective loss function to optimize the SMLGN method and achieve consistency in data distribution. The experimental results demonstrate that the SMLGN method performs well in the task of cross-modal retrieval between RS images and textual data. It effectively integrates the information from both modalities, improving the accuracy and reliability of the retrieval process.
Keyword ：
Adversarial learning Adversarial learning Adversarial machine learning Adversarial machine learning feature fusion feature fusion Green buildings Green buildings Index Terms-Adversarial learning Index Terms-Adversarial learning modality alignment modality alignment multisubspace joint learning multisubspace joint learning Remote sensing Remote sensing remote sensing (RS) image-text (I2T) retrieval remote sensing (RS) image-text (I2T) retrieval Roads Roads Semantics Semantics Sensors Sensors Task analysis Task analysis
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| GB/T 7714 | Chen, Yaxiong , Huang, Jirui , Xiong, Shengwu et al. Integrating Multisubspace Joint Learning With Multilevel Guidance for Cross-Modal Retrieval of Remote Sensing Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Chen, Yaxiong et al. "Integrating Multisubspace Joint Learning With Multilevel Guidance for Cross-Modal Retrieval of Remote Sensing Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Chen, Yaxiong , Huang, Jirui , Xiong, Shengwu , Lu, Xiaoqiang . Integrating Multisubspace Joint Learning With Multilevel Guidance for Cross-Modal Retrieval of Remote Sensing Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Hyperspectral remote sensing is a key technology for remotely obtaining the physical parameters of ground objects and realizing fine identification. It can not only get geometrical properties of the target scenes but also obtain radiance that reflects the characteristics of ground objects. With the development of hyperspectral remote sensing data to unprecedented spatial, spectral, temporal resolution and large data volume, how to adapt to the requirements of massive data and achieve efficient and rapid processing of hyperspectral remote sensing data has become the current research focus. Researchers are introducing scene classification into hyperspectral image classification, integrating the spatial and spectral information to obtain semantic information oriented to larger observation units. However, almost all existing multispectral/hyperspectral scene classification datasets have a number of limitations, including inconsistent spectral and spatial resolutions or spatial resolutions too large to meet the needs of fine-grained classification. Based on the hyperspectral images of Xi’an taken by the 'Zhuhai-1' constellation, we combine the result of unsupervised spectral clustering and Google Earth to establish a hyperspectral satellite image scene classification dataset named HSCD-ZH (Hyperspectral Scene Classification Dataset from Zhuhai-1). It consists of 737 images divided into six categories: urban, agriculture, rural, forest, water, and unused land. Each image with a size of 64 × 64 pixels consists of 32 bands covering the wavelength in the range of 400—1000 nm. In addition, we conduct spatial-based and spectral-based experiments to analyze the performance of existing datasets, and the benchmark results are reported as a valuable baseline for subsequent research. We choose false-color image for the spatial-based experiments and then use popular deep and non-deep learning scene classification techniques. In the experiments based on spectral, the spectral vectors at the pixel are directly used as local spectral features, and BoVW, IFK, and LLC are used to encode them to generate global representations for the scene. Using SVM as the classifier, the optimal overall classification achieved by the two experiments on the proposed dataset is 92.34% and 88.96%, respectively. Considering that those methods have a large amount of information loss, we cascade the features extracted by the two approaches to generate spatial-spectral features. The highest overall accuracy obtained reaches 94.64%, which is the highest improvement in overall accuracy compared to the other datasets. We construct HSCD-ZH by effectively exploiting both spectral and spatial features of hyperspectral images, selecting various scenes that either have representative spectral compositions, clear spatial textures, or both. It has the advantages of big intraclass diversity, strong scalability, and adapting to satellite hyperspectral intelligent information extraction requirements. Both dataset and experiments can provide effective data support for remote sensing scene classification research in the hyperspectral field. Meanwhile, experiments can indicate that extracting features based on spatial or spectral misses a large amount of available information, and integrating the features extracted by the two methods can compensate for this deficiency. In our future work, we aim to expand the number of categories and images of HSCD-ZH and continue to explore algorithms for integrating spatial and spectral information that can accelerate the interpretation and efficient exploitation of hyperspectral scene cubes. © 2024 Science Press. All rights reserved.
Keyword ：
Benchmarking Benchmarking Classification (of information) Classification (of information) Clustering algorithms Clustering algorithms Deep learning Deep learning Feature extraction Feature extraction Image classification Image classification Large datasets Large datasets Pixels Pixels Remote sensing Remote sensing Semantics Semantics Support vector machines Support vector machines
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| GB/T 7714 | Liu, Yuan , Zheng, Xiangtao , Lu, Xiaoqiang . Hyperspectral scene classification dataset based on Zhuhai-1 images [J]. | National Remote Sensing Bulletin , 2024 , 28 (1) : 306-319 . |
| MLA | Liu, Yuan et al. "Hyperspectral scene classification dataset based on Zhuhai-1 images" . | National Remote Sensing Bulletin 28 . 1 (2024) : 306-319 . |
| APA | Liu, Yuan , Zheng, Xiangtao , Lu, Xiaoqiang . Hyperspectral scene classification dataset based on Zhuhai-1 images . | National Remote Sensing Bulletin , 2024 , 28 (1) , 306-319 . |
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遥感对地观测中普遍存在多平台、多传感器和多角度的多源数据，为遥感场景解译提供协同互补信息。然而，现有的场景解译方法需要根据不同遥感场景数据训练模型，或者对测试数据标准化以适应现有模型，训练成本高、响应周期长，已无法适应多源数据协同解译的新阶段。跨域遥感场景解译将已训练的老模型迁移到新的应用场景，通过模型复用以适应不同场景变化，利用已有领域的知识来解决未知领域问题。本文以跨域遥感场景解译为主线，综合分析国内外文献，结合场景识别和目标识别两个典型任务，论述国内外研究现状、前沿热点和未来趋势，梳理总结跨域遥感场景解译的常用数据集和统一的实验设置。本文实验数据集及检测结果的公开链接为：https://github.com/XiangtaoZheng/CDRSSI。
Keyword ：
分布外泛化 分布外泛化 多样性数据集 多样性数据集 模型泛化 模型泛化 自适应算法 自适应算法 跨域遥感场景解译 跨域遥感场景解译 迁移学习 迁移学习
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| GB/T 7714 | 郑向涛 , 肖欣林 , 陈秀妹 et al. 跨域遥感场景解译研究进展 [J]. | 中国图象图形学报 , 2024 , 29 (06) : 1730-1746 . |
| MLA | 郑向涛 et al. "跨域遥感场景解译研究进展" . | 中国图象图形学报 29 . 06 (2024) : 1730-1746 . |
| APA | 郑向涛 , 肖欣林 , 陈秀妹 , 卢宛萱 , 刘小煜 , 卢孝强 . 跨域遥感场景解译研究进展 . | 中国图象图形学报 , 2024 , 29 (06) , 1730-1746 . |
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Hyperspectral image change detection (HSI-CD) is a fundamental task in the field of remote sensing (RS) observation, which utilizes the rich spectral and spatial information in bitemporal HSIs to detect subtle changes on the Earth's surface. However, modern deep learning (DL)-based HSI-CD methods mostly rely on patch-based methods, which leads to spectral band redundancy and spatial information noise in limited receiving domains, thus ignoring the extraction and utilization of saliency information and limiting the improvement of CD performance. To address these issues, this article proposes a joint saliency temporal-spatial-spectral information network (STSS-Net) for HSI-CD. The principal contributions of this article can be summarized: 1) we have designed a spatial saliency information extraction (SSIE) module for denoising based on distance from center pixels and spectral similarity of the substance, which increases the attention to spatial differences between similar spectral substances and different spectral substances; 2) we have designed a compact high-level spectral information tokenizer (CHLSIT) for spectral saliency information, where the high-level conceptual information of changes in spectral interest can be represented by nonlinear combinations of spectral bands, and redundancy can be removed by extracting high-level spectral conceptual features; and 3) utilizing the advantages of CNN and transformer architectures to combine temporal-spatial-spectral information. The experimental results on three real HSI-CD datasets show that STSS-Net can improve the accuracy of CD and has a certain improvement in the detection of edge information and complex information.
Keyword ：
Attention Attention change detection change detection convolutional neural networks (CNNs) convolutional neural networks (CNNs) hyperspectral image (HSI) hyperspectral image (HSI) saliency information saliency information transformer transformer
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| GB/T 7714 | Chen, Yaxiong , Zhang, Zhipeng , Dong, Le et al. A Joint Saliency Temporal-Spatial-Spectral Information Network for Hyperspectral Image Change Detection [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Chen, Yaxiong et al. "A Joint Saliency Temporal-Spatial-Spectral Information Network for Hyperspectral Image Change Detection" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Chen, Yaxiong , Zhang, Zhipeng , Dong, Le , Xiong, Shengwu , Lu, Xiaoqiang . A Joint Saliency Temporal-Spatial-Spectral Information Network for Hyperspectral Image Change Detection . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Aerial scene classification, aiming at assigning a specific semantic class to each aerial image, is a fundamental task in the remote sensing community. Aerial scene images have more diverse and complex geological features. While some statistics of images can be well fit using convolution, it limits such models to capturing the global context hidden in aerial scenes. Furthermore, to optimize the feature space, many methods add class information to the feature embedding space. However, they seldom combine model structure with class information to obtain more separable feature representations. In this article, we propose to address these limitations in a unified framework (i.e., CGFNet) from two aspects: focusing on the key information of input images and optimizing the feature space. Specifically, we propose a global-group attention module (GGAM) to adaptively learn and selectively focus on important information from input images. GGAM consists of two parallel branches: the adaptive global attention branch (AGAB) and the region-aware attention branch (RAAB). AGAB utilizes an adaptive pooling operation to better model the global context in aerial scenes. As a supplement to AGAB, RAAB combines grouping features with spatial attention to spatially enhance the semantic distribution of features (i.e., selectively focus on effective regions of features and ignore irrelevant semantic regions). In parallel, a focal attention loss (FA-Loss) is exploited to introduce class information into attention vector space, which can improve intraclass consistency and interclass separability. Experimental results on four publicly available and challenging datasets demonstrate the effectiveness of our method.
Keyword ：
Aerial scene classification Aerial scene classification attention attention convolutional neural networks (CNNs) convolutional neural networks (CNNs) loss function loss function remote sensing remote sensing
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| GB/T 7714 | Zhao, Yichen , Chen, Yaxiong , Rong, Yi et al. Global-Group Attention Network With Focal Attention Loss for Aerial Scene Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Zhao, Yichen et al. "Global-Group Attention Network With Focal Attention Loss for Aerial Scene Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Zhao, Yichen , Chen, Yaxiong , Rong, Yi , Xiong, Shengwu , Lu, Xiaoqiang . Global-Group Attention Network With Focal Attention Loss for Aerial Scene Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Remote-sensing image-text (RSIT) retrieval involves the use of either textual descriptions or remote-sensing images (RSI) as queries to retrieve relevant RSIs or corresponding text descriptions. Many traditional cross-modal RSIT retrieval methods tend to overlook the importance of capturing salient information and establishing the prior similarity between RSIs and texts, leading to a decline in cross-modal retrieval performance. In this article, we address these challenges by introducing a novel approach known as multiscale salient image-guided text alignment (MSITA). This approach is designed to learn salient information by aligning text with images for effective cross-modal RSIT retrieval. The MSITA approach first incorporates a multiscale fusion module and a salient learning module to facilitate the extraction of salient information. In addition, it introduces an image-guided text alignment (IGTA) mechanism that uses image information to guide the alignment of texts, enabling the effective capture of fine-grained correspondences between RSI regions and textual descriptions. In addition to these components, a novel loss function is devised to enhance the similarity across different modalities and reinforce the prior similarity between RSIs and texts. Extensive experiments conducted on four widely adopted RSIT datasets affirm that the MSITA approach significantly enhances cross-modal RSIT retrieval performance in comparison to other state-of-the-art methods.
Keyword ：
Cross-modal retrieval Cross-modal retrieval image-guided text alignment (IGTA) image-guided text alignment (IGTA) prior similarity prior similarity salient learning salient learning
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| GB/T 7714 | Chen, Yaxiong , Huang, Jinghao , Li, Xiaoyu et al. Multiscale Salient Alignment Learning for Remote-Sensing Image-Text Retrieval [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Chen, Yaxiong et al. "Multiscale Salient Alignment Learning for Remote-Sensing Image-Text Retrieval" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Chen, Yaxiong , Huang, Jinghao , Li, Xiaoyu , Xiong, Shengwu , Lu, Xiaoqiang . Multiscale Salient Alignment Learning for Remote-Sensing Image-Text Retrieval . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Unlike conventional hyperspectral image (HSI) classification in general scenes, agricultural HSI classification poses greater challenges due to the increased occurrence of "same spectrum different object" and "different spectrum same object" phenomena caused by class similarities. Furthermore, the dense spatial distribution of land cover categories in agricultural scenes and the mixing of spatial-spectral features at crop boundaries add to the complexity of agricultural HSIs. To tackle these issues, we propose SANet, a network designed to enhance crop classification. SANet integrates spectral and contextual information while emphasizing self-correlation within the HSIs. It combines the spatial-spectral nonlocal block structure and the multiscale spectral self-attention (SSA) structure, allocating more attention resources to spatial and spectral dimensions and modeling the existing correlations within the spectral-spatial domain. Additionally, we introduce a two-branch spatial-spectral semantic extraction and fusion structure that can adaptively learn results from both branches. Experimental results demonstrate the promising performance of SANet in agricultural HSI classification by effectively utilizing spectral data, contextual information, and self-attention mechanisms.
Keyword ：
Agriculture hyperspectral image (HSI) classification Agriculture hyperspectral image (HSI) classification Correlation Correlation Crops Crops deep learning (DL) deep learning (DL) Feature extraction Feature extraction Hyperspectral imaging Hyperspectral imaging nonlocal self-attention nonlocal self-attention Semantics Semantics Task analysis Task analysis transformer transformer Transformers Transformers
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| GB/T 7714 | Zhang, Bo , Chen, Yaxiong , Li, Zhiheng et al. SANet: A Self-Attention Network for Agricultural Hyperspectral Image Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Zhang, Bo et al. "SANet: A Self-Attention Network for Agricultural Hyperspectral Image Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Zhang, Bo , Chen, Yaxiong , Li, Zhiheng , Xiong, Shengwu , Lu, Xiaoqiang . SANet: A Self-Attention Network for Agricultural Hyperspectral Image Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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