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学者姓名：卢孝强
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Abstract ：
The multiple change detection (MCD) of hyperspectral images (HSIs) is the process of detecting change areas and providing "from-to" change information of HSIs obtained from the same area at different times. HSIs have hundreds of spectral bands and contain a large amount of spectral information. However, current deep-learning-based MCD methods do not pay special attention to the interspectral dependency and the effective spectral bands of various land covers, which limits the improvement of HSIs' change detection (CD) performance. To address the above problems, we propose a spectrum-induced transformer-based feature learning (STFL) method for HSIs. The STFL method includes a spectrum-induced transformer-based feature extraction module (STFEM) and an attention-based detection module (ADM). First, the 3D-2D convolutional neural networks (CNNs) are used to extract deep features, and the transformer encoder (TE) is used to calculate self-attention matrices along the spectral dimension in STFEM. Then, the extracted deep features and the learned self-attention matrices are dot-multiplied to generate more discriminative features that take the long-range dependency of the spectrum into account. Finally, ADM mines the effective spectral bands of the difference features learned from STFEM by the attention block (AB) to explore the discrepancy of difference features and uses the softmax function to identify multiple changes. The proposed STFL method is validated on two hyperspectral datasets, and their experiments illustrate the superiority of the proposed STFL method over the currently existing MCD methods.
Keyword ：
Attention Attention deep learning deep learning hyperspectral images (HSIs) hyperspectral images (HSIs) multiple change detection (MCD) multiple change detection (MCD) transformer transformer
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| GB/T 7714 | Zhang, Wuxia , Zhang, Yuhang , Gao, Shiwen et al. Spectrum-Induced Transformer-Based Feature Learning for Multiple Change Detection in Hyperspectral Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Zhang, Wuxia et al. "Spectrum-Induced Transformer-Based Feature Learning for Multiple Change Detection in Hyperspectral Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Zhang, Wuxia , Zhang, Yuhang , Gao, Shiwen , Lu, Xiaoqiang , Tang, Yi , Liu, Shihu . Spectrum-Induced Transformer-Based Feature Learning for Multiple Change Detection in Hyperspectral Images . | 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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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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It is a challenging task to recognize novel categories with only a few labeled remote-sensing images. Currently, meta-learning solves the problem by learning prior knowledge from another dataset where the classes are disjoint. However, the existing methods assume the training dataset comes from the same domain as the test dataset. For remote-sensing images, test datasets may come from different domains. It is impossible to collect a training dataset for each domain. Meta-learning and transfer learning are widely used to tackle the few-shot classification and the cross-domain classification, respectively. However, it is difficult to recognize novel categories from various domains with only a few images. In this article, a domain mapping network (DMN) is proposed to cope with the few-shot classification under domain shift. DMN trains an efficient few-shot classification model on the source domain and then adapts the model to the target domain. Specifically, dual autoencoders are exploited to fit the source and target domain distribution. First, DMN learns an autoencoder on the source domain to fit the source domain distribution. Then, a target autoencoder is initiated from the source domain autoencoder and further updated with a few target images. To ensure the distribution alignment, cycle-consistency losses are proposed to jointly train the source autoencoder and target autoencoder. Extensive experiments are conducted to validate the generalizable and superiority of the proposed method.
Keyword ：
Adaptation models Adaptation models Cross-domain classification Cross-domain classification few-shot classification few-shot classification Image recognition Image recognition Measurement Measurement meta-learning meta-learning Metalearning Metalearning Remote sensing Remote sensing remote sensing scene classification remote sensing scene classification Task analysis Task analysis Training Training transfer learning transfer learning
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| GB/T 7714 | Lu, Xiaoqiang , Gong, Tengfei , Zheng, Xiangtao . Domain Mapping Network for Remote Sensing Cross-Domain Few-Shot Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Lu, Xiaoqiang et al. "Domain Mapping Network for Remote Sensing Cross-Domain Few-Shot Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Lu, Xiaoqiang , Gong, Tengfei , Zheng, Xiangtao . Domain Mapping Network for Remote Sensing Cross-Domain Few-Shot Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Semantic segmentation of ultrahigh-resolution (UHR) remote sensing images is a fundamental task for many downstream applications. Achieving precise pixel-level classification is paramount for obtaining exceptional segmentation results. This challenge becomes even more complex due to the need to address intricate segmentation boundaries and accurately delineate small objects within the remote sensing imagery. To meet these demands effectively, it is critical to integrate two crucial components: global contextual information and spatial detail feature information. In response to this imperative, the multilevel context-aware segmentation network (MCSNet) emerges as a promising solution. MCSNet is engineered to not only model the overarching global context but also extract intricate spatial detail features, thereby optimizing segmentation outcomes. The strength of MCSNet lies in its two pivotal modules, the spatial detail feature extraction (SDFE) module and the refined multiscale feature fusion (RMFF) module. Moreover, to further harness the potential of MCSNet, a multitask learning approach is employed. This approach integrates boundary detection and semantic segmentation, ensuring that the network is well-rounded in its segmentation capabilities. The efficacy of MCSNet is rigorously demonstrated through comprehensive experiments conducted on two established international society for photogrammetry and remote sensing (ISPRS) 2-D semantic labeling datasets: Potsdam and Vaihingen. These experiments unequivocally establish MCSNet stands as a pioneering solution, that delivers state-of-the-art performance, as evidenced by its outstanding mean intersection over union (mIoU) and mean $F1$ -score (mF1) metrics. The code is available at: https://github.com/WUTCM-Lab/MCSNet.
Keyword ：
Cascade Cascade multilevel fusion multilevel fusion multitask learning multitask learning remote sensing remote sensing Semantics Semantics semantic segmentation semantic segmentation
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| GB/T 7714 | Chen, Yaxiong , Wang, Yujie , Xiong, Shengwu et al. Integrating Detailed Features and Global Contexts for Semantic Segmentation in Ultrahigh-Resolution Remote Sensing Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Chen, Yaxiong et al. "Integrating Detailed Features and Global Contexts for Semantic Segmentation in Ultrahigh-Resolution Remote Sensing Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Chen, Yaxiong , Wang, Yujie , Xiong, Shengwu , Lu, Xiaoqiang , Zhu, Xiao Xiang , Mou, Lichao . Integrating Detailed Features and Global Contexts for Semantic Segmentation in Ultrahigh-Resolution Remote Sensing Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Remote-sensing (RS) scene classification aims to classify RS images with similar scene characteristics into one category. Plenty of RS images are complex in background, rich in content, and multiscale in target, exhibiting the characteristics of both intraclass separation and interclass convergence. Therefore, discriminative feature representations designed to highlight the differences between classes are the key to RS scene classification. Existing methods represent scene images by extracting either global context or discriminative part features from RS images. However, global-based methods often lack salient details in similar RS scenes, while part-based methods tend to ignore the relationships between local ground objects, thus weakening the discriminative feature representation. In this article, we propose to combine global context and part-level discriminative features within a unified framework called CGINet for accurate RS scene classification. To be specific, we develop a light context-aware attention block (LCAB) to explicitly model the global context to obtain larger receptive fields and contextual information. A co-enhanced loss module (CELM) is also devised to encourage the model to actively locate discriminative parts for feature enhancement. In particular, CELM is only used during training and not activated during inference, which introduces less computational cost. Benefiting from LCAB and CELM, our proposed CGINet improves the discriminability of features, thereby improving classification performance. Comprehensive experiments over four benchmark datasets show that the proposed method achieves consistent performance gains over state-of-the-art (SOTA) RS scene classification methods.
Keyword ：
Attention Attention Context modeling Context modeling Convolutional neural networks Convolutional neural networks convolutional neural networks (CNNs) convolutional neural networks (CNNs) discriminative part discovery discriminative part discovery Feature extraction Feature extraction Remote sensing Remote sensing remote-sensing (RS) remote-sensing (RS) scene classification scene classification Semantics Semantics Technological innovation Technological innovation Training Training
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| GB/T 7714 | Zhao, Yichen , Chen, Yaxiong , Xiong, Shengwu et al. Co-Enhanced Global-Part Integration for Remote-Sensing Scene Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Zhao, Yichen et al. "Co-Enhanced Global-Part Integration for Remote-Sensing Scene Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Zhao, Yichen , Chen, Yaxiong , Xiong, Shengwu , Lu, Xiaoqiang , Zhu, Xiao Xiang , Mou, Lichao . Co-Enhanced Global-Part Integration for Remote-Sensing Scene Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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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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Recently, oriented object detection in remote sensing images has garnered significant attention due to its broad range of applications. Early-oriented object detection adhered to the established general object detection frameworks, utilizing the label assignment strategy based on the horizontal bounding box (HBB) annotations or rotation-agnostic cost function. Such a strategy may not reflect the large aspect ratio and rotation of arbitrary-oriented objects in remote sensing images and require high parameter-tuning efforts in the training process, which will eventually harm the detector performance. Furthermore, the localization quality of oriented objects depends on precise rotation angle prediction, exacerbating the inconsistency between classification and regression tasks in oriented object detection. To address these issues, we propose the Gaussian distribution cost optimal transport assignment (GCOTA) and decoupled layer attention angle head (DLAAH). Specifically, GCOTA utilizes a Gaussian distribution-based cost function for the optimal transport (OT) label assignment in the training process, alleviating the impact of rotation angle and large aspect ratio in remote sensing images. DLAAH predicts rotation angle independently and incorporates layer attention to obtain the task-specific features based on the shared FPN features, enhancing the angle prediction and improving consistency across different tasks. Based on these proposed components, we present an anchor-free oriented detector, namely, Gaussian distribution and task-decoupled head oriented detector (GTDet) and a multiclass ship detection dataset in real scenarios (CGWX), which provides a benchmark for fine-grained object recognition in remote sensing images. Comprehensive experiments are conducted on CGWX and several public challenging datasets, including DOTAv1.0, and HRSC2016, to demonstrate that our method achieves superior performance on oriented object detection tasks. The code is available at https://github.com/WUTCM-Lab/GTDet.
Keyword ：
Anchor-free detector Anchor-free detector deep convolution neural networks deep convolution neural networks oriented object detection oriented object detection remote sensing images remote sensing images
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| GB/T 7714 | Huang, Qiangqiang , Yao, Ruilin , Lu, Xiaoqiang et al. Oriented Object Detector With Gaussian Distribution Cost Label Assignment and Task-Decoupled Head [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Huang, Qiangqiang et al. "Oriented Object Detector With Gaussian Distribution Cost Label Assignment and Task-Decoupled Head" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Huang, Qiangqiang , Yao, Ruilin , Lu, Xiaoqiang , Zhu, Jishuai , Xiong, Shengwu , Chen, Yaxiong . Oriented Object Detector With Gaussian Distribution Cost Label Assignment and Task-Decoupled Head . | 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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