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学者姓名：邹长忠
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Abstract ：
Change detection (CD) has a significant application in the remote sensing field. Because of the popularity of hyperspectral image (HSI) and the application of deep learning methods, hyperspectral image change detection (HSI-CD) techniques have been greatly developed. Among them, convolutional neural network (CNN) has garnered the greatest interest in HSI-CD due to their superior feature learning capabilities. However, current CNN-based algorithms have trouble capturing spectral similarity and long-range dependency owing to their intrinsic structural restrictions. Recently, transformers have been shown to extract global dependency from nature images in an extremely efficient way. But it has some difficulties in handling high-dimensional data, such as HSI. To address these issues, we propose an improved multi-scale and spectral-wise transformer (MS-SWT). The proposed MS-SWT is capable of capturing spectral similarity and long-range dependence between bands to enhance the efficiency of the HSI-CD task. Furthermore, to maximize the utilization of spatial information, we present a multi-scale feature fusion module (MFFM) to extract and fuse different dimensions of spatial features. More importantly, a locality self-attention (LSA) module is employed to alleviate the problem of smoothing the distribution of attention scores due to the large number of spectral embeddings. Moreover, we design a channel self-supervised loss function that can capture intrinsic information from the spectral channels to further strengthen the robustness of model training when the training samples are scarce. Lastly, comprehensive experiments present the high performance of our MS-SWT on four bitemporal HSI datasets and demonstrate the superiority of MS-SWT over state-of-the-art approaches.
Keyword ：
change detection change detection Hyperspectral image Hyperspectral image loss function loss function transformer transformer
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| GB/T 7714 | Zou, Changzhong , Liang, Wenfeng , Liu, Lei et al. Hyperspectral image change detection based on an improved multi-scale and spectral-wise transformer [J]. | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2024 , 45 (6) : 1903-1924 . |
| MLA | Zou, Changzhong et al. "Hyperspectral image change detection based on an improved multi-scale and spectral-wise transformer" . | INTERNATIONAL JOURNAL OF REMOTE SENSING 45 . 6 (2024) : 1903-1924 . |
| APA | Zou, Changzhong , Liang, Wenfeng , Liu, Lei , Zou, Changwu . Hyperspectral image change detection based on an improved multi-scale and spectral-wise transformer . | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2024 , 45 (6) , 1903-1924 . |
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Semantic change detection (SCD) can recognize the region and the type of changes in remote sensing images. Existing methods are either based on transformer or convolutional neural network (CNN), but due to the size of various ground objects is different, it is necessary to have global modeling ability and local information extraction ability at the same time. Therefore, in this paper we propose a fusion semantic change detection network (FSCD) with both global modeling ability and local information extraction ability by fusing transformer and CNN. A semi-parallel fusion block has also been proposed to construct FSCD. It can not only have global and local features in parallel, but also fuse them as deeply as serial. To better adaptively decide which mechanism is applied to which pixel, we design a self-attention and convolution selection module (ACSM). ACSM is a self-attention mechanism used to selectively combine transformer and CNN. Specifically, the importance of each mechanism is automatically obtained by learning. According to the importance, the mechanism suitable for a pixel is selected, which is better than using either mechanism alone. We evaluate the proposed FSCD on two datasets, and the proposed network has a significant improvement compared with the state-of-the-art network. © 2023
Keyword ：
Change detection Change detection Convolution Convolution Convolutional neural networks Convolutional neural networks Information retrieval Information retrieval Pixels Pixels Remote sensing Remote sensing Semantics Semantics Semantic Web Semantic Web
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| GB/T 7714 | Zou, Changzhong , Wang, Ziyuan . A semi-parallel CNN-transformer fusion network for semantic change detection [J]. | Image and Vision Computing , 2024 , 149 . |
| MLA | Zou, Changzhong et al. "A semi-parallel CNN-transformer fusion network for semantic change detection" . | Image and Vision Computing 149 (2024) . |
| APA | Zou, Changzhong , Wang, Ziyuan . A semi-parallel CNN-transformer fusion network for semantic change detection . | Image and Vision Computing , 2024 , 149 . |
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Hyperspectral images can be widely used in many fields due to their high information richness. However, costly and complex imaging spectrometers limit its growth. Hyperspectral reconstruction aims to obtain the corresponding hyperspectral image from the multispectral image, to reduce the acquisition cost of hyperspectral images. At present, the related works mainly use methods based on deep learning, and they have achieved good results. However, how to fully extract the global spectral and spatial features of hyperspectral images is still not well solved. To address these issues, we propose an Attention and Transformer Complementary Fusion Network (ATCFNet), which is composed of three modules: Multi-angle Input Image Processing (MIIP), Deep Feature Extraction (DFE) and Hyperspectral Reconstruction (HR) modules. Within the DFE module, an improved Transformer module and a novel Multi-scale Spatial Attention (MSA) module are proposed to extract the global spectral relationship and the spatial features of the hyperspectral images, respectively. Moreover, the MIIP module is proposed to extract the features of input multispectral images more effectively and comprehensively. To verify these, we compare the proposed ATCFNet with other excellent reconstruction methods on three hyperspectral image datasets. The results show that our method achieves the best results in these datasets. Code is publicly available at https://github.com/kidder314/ATCFNet.
Keyword ：
Hyperspectral imaging Hyperspectral imaging image reconstruction image reconstruction image resolution image resolution spatial attention spatial attention transformer transformer
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| GB/T 7714 | Zou, Changwu , Zhang, Can , Zou, Changzhong . Attention and transformer complementary fusion network for hyperspectral image spectral reconstruction [J]. | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2024 , 45 (15) : 5095-5112 . |
| MLA | Zou, Changwu et al. "Attention and transformer complementary fusion network for hyperspectral image spectral reconstruction" . | INTERNATIONAL JOURNAL OF REMOTE SENSING 45 . 15 (2024) : 5095-5112 . |
| APA | Zou, Changwu , Zhang, Can , Zou, Changzhong . Attention and transformer complementary fusion network for hyperspectral image spectral reconstruction . | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2024 , 45 (15) , 5095-5112 . |
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In recent years, deep convolutional neural networks (CNNs) have been widely exploited for the hyperspectral image (HSI) super-resolution and obtained remarkable performance. However, most of the existing CNN -based methods have two main problems. One is to use two-dimension (2D) convolution to extract spatial information without paying attention to the mining of spectral information of hyperspectral images. The other is to use three-dimension (3D) convolution, which reduces the efficiency of the model when the network parameters increase. To address the above issues, we propose clustering deep residual neural network (CDRNN) for hyperspectral image super-resolution in this paper. The proposed CDRNN learns the complex, nonlinear mappings between low spatial resolution HSI and high spatial resolution HSI. At first, an unsupervised clustering method is used to divide a low spatial resolution HSI into several classes according to spectral correlation. Then, the spectrum-pairs from the classified low spatial resolution HSI and the corresponding high spatial resolution HSI are used to train the CDRNN to establish the nonlinear mapping for each class. Finally, we classify the given low spatial resolution HSI into the determined category and use the trained CDRNN to reconstruct the final high spatial resolution HSI from the classified low spatial resolution HSI. We conduct extensive experiments on three simulated benchmark datasets and a real HSI to evaluate the super-resolution performance of the proposed method. Experimental results show that our proposed method achieves significant improvement over state-of-the-art methods.
Keyword ：
Clustering Clustering Deep residual neural network Deep residual neural network Hyperspectral image Hyperspectral image Super-resolution Super-resolution
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| GB/T 7714 | Zou, Changzhong , Zhang, Can . Hyperspectral image super-resolution using cluster-based deep convolutional networks [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2023 , 110 . |
| MLA | Zou, Changzhong et al. "Hyperspectral image super-resolution using cluster-based deep convolutional networks" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 110 (2023) . |
| APA | Zou, Changzhong , Zhang, Can . Hyperspectral image super-resolution using cluster-based deep convolutional networks . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2023 , 110 . |
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The growing availability of high-quality remote sensing imagery has led to increased interest in semantic change detection (SCD). Supervised methods for this task have shown significant performance improvements, but acquiring labeled data is often challenging and expensive. To confront this challenge, we propose a semisupervised approach for SCD in remote sensing images using an innovative teacher-student model. We use a convolutional neural network (CNN) in the teacher model and a fusion design combining CNN and vision transformer in the student model, with the rationale that the CNN requires fewer training samples compared with vision transformer, and fusion network allows us to leverage the advantages of both. To further enhance the model's performance, we propose a novel data augmentation approach by interchanging bitemporal images as well as their labels. The principle for that is the change from one moment to another and vice versa are two different changes and can, therefore, be used to augment the training dataset. More importantly, this method does not reduce its reliability, because no noise is brought to the remote sensing images. By adopting this approach, we are able to better utilize the small labeled dataset to increase the precision of the model while maintaining the robustness. According to the experimental results, the proposed method outperforms several state-of-the-art methods and achieves an improvement compared with bi-temporal semantic reasoning network (Bi-SRNet) in mean intersection over union (mIoU)/separated kappa (SeK)/overall accuracy (OA) of 3.25/4.42/1.78, 6.37/11.72/2.91 on the SECOND and Landsat-SCD datasets, respectively.
Keyword ：
Convolutional neural network (CNN) Convolutional neural network (CNN) semantic change detection (SCD) semantic change detection (SCD) semisupervised semisupervised vision transformer vision transformer
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| GB/T 7714 | Zou, Changzhong , Wang, Ziyuan . A New Semisupervised Method for Detecting Semantic Changes in Remote Sensing Images [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2023 , 20 . |
| MLA | Zou, Changzhong et al. "A New Semisupervised Method for Detecting Semantic Changes in Remote Sensing Images" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20 (2023) . |
| APA | Zou, Changzhong , Wang, Ziyuan . A New Semisupervised Method for Detecting Semantic Changes in Remote Sensing Images . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2023 , 20 . |
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Quickly extracting road networks from high-resolution remote sensing images is crucial in mapping, urban planning, and GIS databases updating. Semi-automatic road extraction, as the main method of road surveying and mapping, is a labor-intensive task. In order to reduce the cost of manual intervention and improve extraction efficiency, this paper proposes a fast road centerline extraction algorithm based on geodesic distance field. First, the optimal circular template is proposed to automatically estimated the road width and adjust the manual seeds to road center based on the morphological gradient map, and the road saliency map is calculated according to the local color features inside the templates. Second, we propose the soft road center kernel density based on road saliency map which overcomes the difficulty of threshold presetting of road segmentation in traditional road center kernel density estimation. Most importantly, a geodesic distance field is proposed to quickly extract the geodesic curve between two consecutive seeds, which dramatically increase the efficiency of our algorithm. Finally, we introduce the mean filter into our scheme to smooth the road centerlines. Extensive experiments and quantitative comparisons show that the proposed algorithm can greatly reduce manual intervention without losing much accuracy, and significantly improve the efficiency of road extraction. Furthermore, the proposed algorithm takes almost the same time to extract any length of road centerline given fixed image size, and no hyperparameters need to be set. The algorithm behaves good experience in human-computer interaction. © 2023 SinoMaps Press. All rights reserved.
Keyword ：
Curve fitting Curve fitting Efficiency Efficiency Extraction Extraction Geodesy Geodesy Human computer interaction Human computer interaction Mapping Mapping Remote sensing Remote sensing Roads and streets Roads and streets
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| GB/T 7714 | Lian, Renbao , Zhang, Zhenmin , Liao, Yipeng et al. A quick road centreline extraction method from remote sensing images combining with geodesic distance field and curve smoothing [J]. | Acta Geodaetica et Cartographica Sinica , 2023 , 52 (8) : 1317-1329 . |
| MLA | Lian, Renbao et al. "A quick road centreline extraction method from remote sensing images combining with geodesic distance field and curve smoothing" . | Acta Geodaetica et Cartographica Sinica 52 . 8 (2023) : 1317-1329 . |
| APA | Lian, Renbao , Zhang, Zhenmin , Liao, Yipeng , Zou, Changzhong , Huang, Liqin . A quick road centreline extraction method from remote sensing images combining with geodesic distance field and curve smoothing . | Acta Geodaetica et Cartographica Sinica , 2023 , 52 (8) , 1317-1329 . |
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Multi-label image classification (MLIC) is a quintessential but challenging issue in the field of Computer Vision. Since the label co-occurrence is a crucial component of MLIC, previous existing approaches resort to the label co-occurrence for either modeling label correlations or modeling visual feature relationships. However, these methods ignore either the feature interaction or the label characteristics in MLIC. In this paper, we propose a label-aware graph representation learning (LGR) for MLIC that can explore the label interaction via a graph neural network built on the label co-occurrence and mine the feature correlations via another graph neural network also based on the label co-occurrence. Moreover, to decouple semantic visual features, current approaches resort to the word embedding guided semantic decoupling methods. However, the word embedding cannot clearly represent the label semantic information of MLIC. Hence, we reconstruct the semantic decoupling method by using the graph label representation. Extensive experiments on three benchmark datasets well demonstrate that our proposed framework can signifi-cantly achieve the state-of-the-art performance. In addition, a series of ablative studies further demon-strate the positive impacts of our proposed model.(c) 2022 Elsevier B.V. All rights reserved.
Keyword ：
Graph neural network Graph neural network Graph representation Graph representation Multi-label image classification Multi-label image classification Semantic decoupling Semantic decoupling
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| GB/T 7714 | Chen, Yilu , Zou, Changzhong , Chen, Jianli . Label-aware graph representation learning for multi-label image classification [J]. | NEUROCOMPUTING , 2022 , 492 : 50-61 . |
| MLA | Chen, Yilu et al. "Label-aware graph representation learning for multi-label image classification" . | NEUROCOMPUTING 492 (2022) : 50-61 . |
| APA | Chen, Yilu , Zou, Changzhong , Chen, Jianli . Label-aware graph representation learning for multi-label image classification . | NEUROCOMPUTING , 2022 , 492 , 50-61 . |
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Road digitizing is a labor-intensive task. For reducing labor cost, this letter presents an effective semiautomatic road delineation method based on geodesic distance field (GDF) and piecewise polygon fitting. The main components of the approach are optimal circle calculation, soft road center kernel density estimation (KDE), fast GDF generation, and piecewise polygon fitting model. First, the adaptive circular template is proposed to automatically measure the road width. Next, the soft road center kernel density is estimated for fast GDF generation, which supports the extraction of road centerline between two adjacent seeds. Finally, piecewise polygon fitting is used to refine the road centerline. Extensive experiments demonstrate that the proposed algorithm is efficient and robust in road delineation. The proposed approach takes almost the same time to extract any length of road segment given fixed image size, and no hyperparameters needs to be set.
Keyword ：
Adaptation models Adaptation models Adaptive circular template Adaptive circular template Estimation Estimation Fitting Fitting geodesic distance field (GDF) geodesic distance field (GDF) high-resolution remote sensing images high-resolution remote sensing images Kernel Kernel Manuals Manuals Roads Roads semiautomatic road centerline extraction semiautomatic road centerline extraction Surface fitting Surface fitting
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| GB/T 7714 | Lian, Renbao , Zhang, Zhenmin , Zou, Changzhong et al. An Effective Road Centerline Extraction Method From VHR [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Lian, Renbao et al. "An Effective Road Centerline Extraction Method From VHR" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Lian, Renbao , Zhang, Zhenmin , Zou, Changzhong , Huang, Liqin . An Effective Road Centerline Extraction Method From VHR . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
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Routability estimation identifies potentially congested areas in advance to achieve high-quality routing solutions. To improve the routing quality, this paper presents a deep learning-based congestion estimation algorithm that applies the estimation to a global router. Unlike existing methods based on traditional compressed 2D features for model training and prediction, our algorithm extracts appropriate 3D features from the placed netlists. Furthermore, an improved RUDY (Rectangular Uniform wire DensitY) method is developed to estimate 3D routing demands. Besides, we develop a congestion estimator by employing a U-net model to generate a congestion heatmap, which is predicted before global routing and serves to guide the initial pattern routing of a global router to reduce unexpected overflows. Experimental results show that the Pearson Correlation Coefficient (PCC) between actual and our predicted congestion is high at about 0.848 on average, significantly higher than the counterpart by 21.14%. The results also show that our guided routing can reduce the respective routing overflows, wirelength, and via count by averagely 6.05%, 0.02%, and 1.18%, with only 24% runtime overheads, compared with the state-of-the-art CUGR global router that can balance routing quality and efficiency very well. In particular, our work provides a new generic machine learning model for not only routing congestion estimation demonstrated in this paper, but also general layout optimization problems. © 2022 IEEE.
Keyword ：
Computer aided design Computer aided design Correlation methods Correlation methods Deep learning Deep learning Three dimensional computer graphics Three dimensional computer graphics
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| GB/T 7714 | Su, Miaodi , Ding, Hongzhi , Weng, Shaohong et al. High-Correlation 3D Routability Estimation for Congestion-guided Global Routing [C] . 2022 : 580-585 . |
| MLA | Su, Miaodi et al. "High-Correlation 3D Routability Estimation for Congestion-guided Global Routing" . (2022) : 580-585 . |
| APA | Su, Miaodi , Ding, Hongzhi , Weng, Shaohong , Zou, Changzhong , Zhou, Zhonghua , Chen, Yilu et al. High-Correlation 3D Routability Estimation for Congestion-guided Global Routing . (2022) : 580-585 . |
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Spectral reconstruction from RGB images has made significant progress. Previous works usually utilized the noise-free RGB images as input to reconstruct the corresponding hyperspectral images (HSIs). However, due to instrumental limitation or atmospheric interference, it is inevitable to suffer from noise (e.g., Gaussian noise) in the actual image acquisition process, which further increases the difficulty of spectral reconstruction. In this article, we propose an enhanced channel attention network (ECANet) to learn a nonlinear mapping from noisy RGB images to clean HSIs. The backbone of our proposed ECANet is stacked with multiple enhanced channel attention (ECA) blocks. The ECA block is the dual residual version of the channel attention block, which makes the network focus on key auxiliary information and features that are more conducive to spectral reconstruction. For the case that the input RGB images are disturbed by Gaussian noise, cross-layer feature fusion unit is used to concatenate the multiple feature maps at different depths for more powerful feature representations. In addition, we design a novel combined loss function as the constraint of the ECANet to achieve more accurate reconstruction result. Experimental results on two HSI benchmarks, CAVE and NTIRE 2020, demonstrate that the effectiveness of our method in terms of both visual and quantitative over other state-of-the-art methods.
Keyword ：
combined loss function combined loss function Convolution Convolution Cross layer design Cross layer design Cross-layer feature fusion (CLFF) Cross-layer feature fusion (CLFF) enhanced channel attention (ECA) enhanced channel attention (ECA) Feature extraction Feature extraction Gaussian noise Gaussian noise Hyperspectral imaging Hyperspectral imaging Image reconstruction Image reconstruction spectral reconstruction spectral reconstruction Training Training
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| GB/T 7714 | Zou, Changwu , Zhang, Can , Wei, Minghui et al. Enhanced Channel Attention Network With Cross-Layer Feature Fusion for Spectral Reconstruction in the Presence of Gaussian Noise [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2022 , 15 : 9497-9508 . |
| MLA | Zou, Changwu et al. "Enhanced Channel Attention Network With Cross-Layer Feature Fusion for Spectral Reconstruction in the Presence of Gaussian Noise" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15 (2022) : 9497-9508 . |
| APA | Zou, Changwu , Zhang, Can , Wei, Minghui , Zou, Changzhong . Enhanced Channel Attention Network With Cross-Layer Feature Fusion for Spectral Reconstruction in the Presence of Gaussian Noise . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2022 , 15 , 9497-9508 . |
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